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SafeAssign Originality Report Summer 2020 - Business Intelligence (ITS-531-06) - First … • Week 4 Assignment

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Assignment #4

Sruthi Dhadvai

University of the Cumberlands

Business Intelligence ITS 531 – 06 Summer 2020 First Bi-Term

Q1: Data mining is considered to be a procedure which depends on algorithms in analyzing as well as extracting information which is useful from given data. Data mining can be utilized in automatically discovering patterns that are hidden in addition to relations in information, as well as predicting results from data sets that are large. Text mining is identified as a set of procedure

needed to convert text documents that are unstructured or resources to structured information which is valuable. Sentiment analysis extracts texts from social networks, online reviews, emails, interactions on call center in addition to various sources of information to identifying threads that are common which point negative or positive feelings on a clients’ part. Sentiment analysis is known to be the study of information which is subjective in a given expression, such as appraisals, opinions, and attitudes in addition to emotion in regard to a certain topic, entity or person. Expressions are either categorized as negative, neutral or positive (Allahyari, et.al, 2017). Q2: Text mining is used to explore as well as analyze huge sums of text data which unstructured assisted by a software which is capable of identifying patterns, concepts, keywords, topics as well as additional attributes within the data. This process is also referred to as text analytics; however, several individuals have a distinction drawn in between both terms. From that perspective, text analytics is considered to be an application which is enabled through utilization of techniques of text mining in sorting through sets of data (Kong and Gerstein, 2018). Applications of text mining include risk management this means integrating as well as adopting software of risk management which is powered through text mining techniques like SAS text miner help enterprises in staying updated generally with trends that are current within the enterprise market. Another application is the customer service; techniques in text mining are getting enhanced importance within the customer care field. Q3: Text analytics is considered to help during the process of building additional structure in addition to metadata across a text which was initially unstructured. Through the addition of extra structure, it becomes possible in deriving more value. Inducing structure basically means first having structure imposed unto the data, thereafter have the structured data mined. Several ways of inducing structure into data include isolation of key words; determining the key topics basically meaning the text has to be classified according to the matter of subject in addition to measuring the sentiment this means having the tone gauged. Q4: NLP basically plays the role of leveraging the tireless computer’s speed into applying analysis which is human like into text. Technologies that are new such as text embedding basically convert words as well as phrases into vectors that are mathematical which make it possible for easy comparing on how both phrases are similar. In simpler terms, NLP allows people to manipulate as well as have texts analyzed in a number like manner. The capabilities of numbers include the fact they are known for being great, this is because it is considered to be easy to add, average, compare in addition to learning all manner of things such as revenue comparisons or consumer trends on their spending within a given period. NLP also has its own limitations which include variety as well as ambiguity in text, data availability nowadays most of NLP tends to be generated through the use of models which are considered to be machine-learned (Lee, et.al, 2020). Exercise: There are several packages which are mainly implemented in the process of text mining as well as data mining which include; Civis- this package is mainly considered to be an end to end, easy to use as well as an extendable

platform of data science which is within the cloud, created by scientists of data, for any team which desires to make great decisions which are driven by data to make it possible for their organizations be driven in the forward direction. Another package which

is used in the process is considered to be the CMSR Data Miner which is basically created for the enterprise data which is known to have database focus, which is also known for incorporating the rule engine, decision tree, neural clustering, neural network, hotspot drill down, cross sell analysis, cross table deviation analysis, charts and visualization in addition to many more. An additional

package which is also used in data mining as well as text mining includes the Coheris SPAD, this package is basically known for providing exploratory analyses which are known to be powerful as well as gadgets for data mining which include clustering, PCA, decision trees that are interactive, analyses that are discriminant, networks that are neural, text mining in addition to many more others, all through GUI which is user friendly.

There is also the package of AdvancedMiner which is from Algolytics, it mainly offers tools of a wider range which are used in

transformations of data, models of mining data, analysis of data as well as reporting of data (Silge and Robinson, 2017).

References

Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., &Kochut, K. (2017). A brief survey of text mining:

Classification, clustering and extraction techniques. arXiv preprint arXiv:1707.02919. Retrieved from

https://arxiv.org/abs/1707.02919

Kong, X., & Gerstein, M. B. (2018). Text mining systems biology: Turning the microscope back on the observer. Current

Opinion in Systems Biology, 11, 117-122. Retrieved from

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https://www.sciencedirect.com/science/article/pii/S2452310018300787

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020).BioBERT: a pre-trained biomedical language

representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240. Retrieved from

https://academic.oup.com/bioinformatics/article/36/4/1234/5566506

Sharda, R., Delen, D., Turban, E., Aronson, J., & Liang, T. (2014). Business intelligence and analytics. System for Decesion

Support. Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. " O'Reilly Media, Inc.". Retrieved from

https://books.google.co.ke/books? hl=en&lr=&id=qNcnDwAAQBAJ&oi=fnd&pg=PP1&dq=text+mining&ots=Q0DPdoJVxY&sig=RgpTzQUatkh- 2e0nqQ6TW6ENTw4&redir_esc=y#v=onepage&q=text%20mining&f=false

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ASSIGNMENT 4 1

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WEEK 4 ASSIGNMENT 1

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ASSIGNMENT 4 2

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Week 4 Assignment

Student paper

University of the Cumberlands

Original source

University of the Cumberlands

Student paper

Business Intelligence ITS 531 – 06 Summer 2020 First Bi-Term

Original source

Business Intelligence ITS 531 – 06 Summer 2020 First Bi-Term University of the Cumberlands

Student paper

Text mining is identified as a set of procedure needed to convert text documents that are unstructured or resources to structured information which is valuable.

Original source

Text mining encompasses a set of processes employed in turning unstructured text resources or documents into valuable, structured information

Student paper

Civis- this package is mainly considered to be an end to end, easy to use as well as an extendable platform of data science which is within the cloud, created by scientists of data, for any team which desires to make great decisions which are driven by data to make it possible for their organizations be driven in the forward direction.

Original source

Civis, an easy-to-use, end-to-end, extendable, data science platform in the cloud, built by data scientists, for teams who want to make great data- driven decisions to drive their organizations forward

Student paper

Another package which is used in the process is considered to be the CMSR Data Miner which is basically created for the enterprise data which is known to have database focus, which is also known for incorporating the rule engine, decision tree, neural clustering, neural network, hotspot drill down, cross sell analysis, cross table deviation analysis, charts and visualization in addition to many more.

Original source

CMSR Data Miner, built for business data with database focus, incorporating rule-engine, neural network, neural clustering (SOM), decision tree, hotspot drill-down, cross table deviation analysis, cross- sell analysis, visualization/charts, and more

kdnuggets 64%

kupdf 73%

Student paper 73%

Student paper 86%

papers 86%

papers 100%

Student paper 88%

Student paper 100%

Student paper 66%

Student paper 75%

Student paper 82%

Student paper 100%

Student paper

An additional package which is also used in data mining as well as text mining includes the Coheris SPAD, this package is basically known for providing exploratory analyses which are known to be powerful as well as gadgets for data mining which include clustering, PCA, decision trees that are interactive, analyses that are discriminant, networks that are neural, text mining in addition to many more others, all through GUI which is user friendly. There is also the package of AdvancedMiner which is from Algolytics, it mainly offers tools of a wider range which are used in transformations of data, models of mining data, analysis of data as well as reporting of data (Silge and Robinson, 2017).

Original source

Coheris SPAD, provides powerful exploratory analyses and data mining tools, including PCA, clustering, interactive decision trees, discriminant analyses, neural networks, text mining and more, all via user-friendly GUI AdvancedMiner from Algolytics, provides a wide range of tools for data transformations, Data Mining models, data analysis and reporting

Student paper

A brief survey of text mining:

Original source

A Survey of Text Mining Techniques and

Student paper

Classification, clustering and extraction techniques.

Original source

Feature extraction, classification, and clustering A

Student paper

Retrieved from https://arxiv.org/abs/1707.02919

Original source

Retrieved from https://arxiv.org/abs/1707.01031

Student paper

Kong, X., & Gerstein, M.

Original source

X Kong, M Gerstein (2018)

Student paper

Text mining systems biology: Turning the microscope back on the observer. Current Opinion in Systems Biology, 11, 117-122.

Original source

Text mining systems biology Turning the microscope back on the observer Current Opinion in Systems Biology 11:117-122

Student paper

Retrieved from https://www.sciencedirect.com/scien ce/article/pii/S2452310018300787

Original source

Retrieved from https://www.sciencedirect.com/scien ce/article/pii/S0167739X16306963

Student paper

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J.

Original source

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C H., & Kang, J

Student paper

a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240.

Original source

a pre-trained biomedical language representation model for biomedical text mining Bioinformatics, 36(4), 1234-1240

Student paper

Retrieved from https://academic.oup.com/bioinfor matics/article/36/4/1234/5566506

Original source

Retrieved from https://academic.oup.com/bioinfor matics/article/33/21/3364/3885699

Student paper

Sharda, R., Delen, D., Turban, E., Aronson, J., & Liang, T.

Original source

Sharda, R., Delen, D., Turban, E

Student paper

Business intelligence and analytics.

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(2012) Business Intelligence and Analytics

Student paper

O'Reilly Media, Inc.".

Original source

O'Reilly Media, Inc."