assignment Useing R
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4peerreview.doc58 minutes ago
Prashanth Yarlagadda
Favorite Visualisations
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The three visualizations I liked are The Ebb and Flow of Movies: Box Office Receipts 1986 — 2008, Migration in the news and migration in the census.
I liked the colors and the way of representation in The Ebb and Flow of Movies: Box Office Receipts 1986 — 2008 visualization. The colors used are not too bright and we can filter on the movie to get more information on the movie. The film's total domestic gross is shown in the visualization. By looking at the visualization, I can get the range of the film's gross but couldn't completely interpret total gross. I would have considered showing the actual total gross value along with other details when the movie is clicked.
Migration in the news visualization is very clean, simple and easy to understand. By looking at the visualization, we can clearly tell the changes in the migrant groups. It also showed the words used to describe the migrants and lets us filter based on migrant group. It also showed the frequency of organizations and people who appeared in the news.
I also liked migration in the census. It has introduction and it gives us an option to skip the intro. We can also filter based on the region and it displays more details like gender distribution, non-UK born and local distribution etc, occupational status etc. Representing in bubbles did not show the name of the regions until we click on the bubble. I might have considered tree map visualization to represent this visualization.
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3 hours ago
Krishna Chaitanya Karamsetty
week 7 discussion
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Understanding Visualizations
The first visualization that I like is, “The clicks don’t lie.” This visualization tries to present the number of followers that Shakira has on twitter, Instagram, and Facebook (seeingdata, n.d). One thing I like about this visualization is that it used objects that are related to the topic. That is the bird to represent twitter, the camera icon to represent Instagram, and ‘F’ to represent Facebook. These objects are very eye-catching since most people nowadays use social media; thus, it will be very easy to draw their attention. What about this visualization that I would change is to use a graph that represents proportions clearly.
The second visualization is the top ten freshwater consumers (seeingdata, n.d). One thing that caught my attention is the correct use of colors. Colors are known to increase the attention of the audience. This visualization effectively represented the top ten freshwater consumers by including the major way the different countries use their water. One thing that I would change about this visualization is to use a visualization that is easy to follow. This graph seems a bit complex, especially for people who are not familiar with visualizations.
The third visualization that I liked is the Non-UK Born Census Populations 1951 – 2011 (seeingdata, n.d). The first thing that caught my attention is the fact that it incorporated various visualizations to represent the data properly. In this way, the different dimensions of data are well represented. This graph was able to visualize the data well. One thing I would change about this visualization is that I would include a time-series graph. Time series is well known for representing the changes that occur over a given period.
References
seeingdata. (n.d). Making sense of data visualisations. Retrieved from seeingdata: http://seeingdata.org/developing-visualisation-literacy/rate-these-visualisations/
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3 hours ago
Amarnath Reddy Kotha
Discussion
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Amarnath Reddy Kotha,
Discussion,
With the fast development of the web and online media, the present brands currently have more admittance to client input than any other time in recent memory. Client created substance, for example, item surveys, online media posts, gatherings, and sites are a goldmine of shopper slant, offering organizations exceptional knowledge into genuine clients' assessments about their items and administrations. Therefore, numerous organizations are presently searching for versatile and savvy approaches to gather and dissect this substance over various channels. Feeling examination, otherwise called supposition mining, is one key system brands are utilizing to achieve this undertaking, and it is helping them make quicker and more educated showcasing and item choices than any time in recent memory. Be that as it may, what is assumption examination, and how can it work In this post, we'll give a short review of famous opinion investigation strategies, their regular uses, and any related difficulties.
The objective of supposition examination is to decide the demeanor of a speaker or essayist dependent on the language they use with respect to a particular subject or item. Different stages can be utilized to investigate whether a slant is positive, negative or impartial. These stages are commonly arranged as either robotized or human based, with numerous organizations utilizing a half and half model. At the point when separated further into explicit strategies for examination, there are advantages and disadvantages to each. Manual preparing requires a human component in the examination, explicitly to help decipher language complexities, for example, setting, uncertainty, mockery and incongruity. While this assists with improving precision of the examination, it is additionally additional tedious and costly.
Catchphrase handling utilizes a calculation that allots an estimation to explicit words. For instance, "incredible" is positive while "terrible" is negative. While the outcomes are conveyed rapidly and cheaply, this strategy doesn't represent complexities, for example, twofold negatives, words with numerous implications, and setting. Normal language handling (NLP), otherwise called text examination or information mining, utilizes programming to dissect words and concentrate their real implications. It mulls over feeling and setting and clings to a lot of rules created because of recognized examples and subjects in language.
References:
https://www.onespace.com/blog/2017/05/what-is-sentiment-analysis-uses-challenges/
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9 hours ago
Sampath Reddy Lachireddygari
Week 7 Discussion
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Sentiment analysis can be characterized as a procedure of utilizing natural language processing, statistics, and text analysis to break down client sentiment. In business terms, it is an assortment of instruments to recognize and hear the thoughts to utilize them for business purposes. A large portion of the organizations attempt to comprehend the conclusion of their clients what are the audits, how they have given those surveys, and what precisely they mean. The most widely recognized difficulties that sentiment analysis deals are, the idealness of examination of the literary type of information which is originating from various sources, the notion which shows up in the content as a rule is of two structures: unequivocal (abstract sentence clarifies the sentiment.) and understood ( text infers a supposition ).The sentiment analysis is difficult to break down as we don't know of the assessments or decisions (M. Ebrahimi., 2020). Another test is that it is difficult to recognize if a specific book includes supposition or not, particularly when sentiment analysis is performed.
The most well-known application zones for sentiment analysis incorporate decision making, highlighting competitive advantage, predicting product life cycle, and improving client experience. On decision-making, sentiment analysis gives understanding on any adjustment in popular feeling identified with your image that will either bolster or discredit the bearing your business is going. For highlighting competitive advantage, sentiment analysis can help anticipate client inclines and improve such that will put an association in front of the contenders.
When sentiment analysis didn't exist, the old traditional sentiment analysis is based on a survey or focused group centered which are both expensive and time taking process but with the new technology-based methods such as using NLP methodologies and data mining, Sentiment analysis is an important application for text analytics. It is used broadly in analyzing tweets, Facebook posts, product reviews.
References
M. Ebrahimi, A. Yazdavar, and A. Sheth, On the Challenges of Sentiment Analysis for Dynamic events, IEEE Intelligent Systems, 32(5), 2017
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