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Title: IFT-598 Module 6: Certainly Not Groupthink
Group : huit
Course Title: IFT 598 Natural Language Processing
Section: Wed (4.30 PM – 7.15 PM)
Professor Name: Prof. Brian Atkinson
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Submission Date: 02-31-2024
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• Please share the progress that you have made on the project. There is no expectation here,
it
serves as a simple "check-in"
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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5
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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6
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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7
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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8
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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9
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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10
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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11
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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12
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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13
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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14
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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15
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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16
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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17
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
18
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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19
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
20
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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21
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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22
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
23
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
24
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
25
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
26
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
P a g e |
27
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
P a g e |
28
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
29
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
30
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
31
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
32
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
P a g e |
33
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
P a g e |
34
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
35
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
36
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
37
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
38
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
P a g e |
39
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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40
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
41
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
42
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
43
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
44
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
P a g e |
45
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
P a g e |
46
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
47
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
48
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
49
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
50
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
P a g e |
51
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
P a g e |
52
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
53
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
54
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
55
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
56
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
P a g e |
57
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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59
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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60
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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62
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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63
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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64
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
65
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
66
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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67
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
68
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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69
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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70
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
71
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
72
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
P a g e |
73
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
P a g e |
74
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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75
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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80
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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83
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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84
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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85
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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86
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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87
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
89
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
90
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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91
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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92
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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93
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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94
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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101
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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102
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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104
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
107
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
P a g e |
108
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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109
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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110
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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111
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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112
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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119
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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120
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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122
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
125
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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126
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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128
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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130
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
131
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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138
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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140
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
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143
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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144
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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145
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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146
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
P a g e |
149
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
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We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
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the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
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152
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
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153
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
We have been able to define the problem and establish goals for it thus far in this project. We
conducted literature reviews and established the project's scope based on that. In addition, we
have instruments at our disposal and a plan in place for gathering data. We've also decided on
the method we'll use to train our dataset and get tweets out of Twitter via its APIs.
• Document any issues you are encountering.
We are now considering focusing only on a specific domain for our issue in case we are unable to
come up with a workable answer. But we can talk about this amongst ourselves. While extracting
the tweet data from Twitter, we may choose any topic we want to look for by using hashtags (#)
or username filters to limit down the domain. Furthermore, the 1.6 million-row dataset we used
to train our model on is currently proving to be too big. As a result, we are thinking about
choosing a sample from our dataset that is manageably big for training and doesn't require a lot
of processing time.
• Solicit and document any help, clarification, or direction that you might need.
We don't appear to need any assistance or clarification on the aforementioned matters at this
time. If we do encounter any other problems, we will attempt to address and resolve them
among our group members. We will get in touch with you if it appears that we need assistance
and direction.
• How viable do you see the topic that you have chosen?
We feel that the topic we have selected, "Sentiment Analysis," is a good fit for our research
because there are a number of materials and algorithms available. Additionally, we have access
to pre-labeled datasets on which to train our prediction model.
• Do you need to fine tune your topic?
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154
Although our topic does not specifically cover sentiment analysis, we believe that it needs to be
refined. We can focus on a certain topic to address a particular domain by narrowing down our
topic.
• What needs adjustment?
We are considering selecting a sample from our dataset that is large enough to train with
appropriate accuracy but does not take too long to run. As a result, our training dataset may
require some changes. We'll utilize Twitter to extract tweets, and we may choose the
specific topic to perform sentiment analysis on.
•
• Does everyone in your group agree?
Yes, we have unanimously decided on the topic, flow, and the working of the project.
