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Yogi R. Patel

Interests
  • Business analysis
  • Client server
  • Cloud computing
  • Collaborative systems
    • more...
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FLASH RESEARCH PAPERS
As part of the Enterprise IT Architecture (MIS 2501) curriculum, students are required to write business proposals to company CIOs to invest in new technologies. In the one-page paper, students communicate to the CIO the costs of the current and proposed new technology, the benefits of investing in the new technology, and the net benefits over a period of three years. Students also present the value proposition of the new technology and how it benefits the company. Through these papers, students enhance their business communication and writing skills along with learning how to effective communicate complex technologies to the general body.

 

Flash Research Paper 1 – Data Centers and Networking

Flash Research Paper 2 – Virtualization and Cloud Computing

Flash Research Paper 3 – SharePoint

Flash Research Paper 4 – WordPress

MIS3501 – Data-Centric Application Development 

  • Developed window shopping website utilizing HTML5, CSS3, PHP, and SQL

Link to website: http://misdemo.temple.edu/tuf86408/sonny_shines/

Text Mining and Sentiment Analysis

 

Text mining is the process of analyzing and filtering relatable text from many forums such as feedback forms, Facebook posts, Tweets, Instagram posts, and any other medium used by a person to express themselves. Sentiment analysis stems from text mining. After the text is filtered, sentiment analysis is used to determine the attitude of a speaker or writer. Sentiment analysis is crucial to data analytics because it is a form of data analytics. Many companies text mine and collect data and sentiment analysis analyzes the collected data to make good use of the data.

Text mining and sentiment analysis relates to many topics discussed in the MIS2502 Data Analytics course. One of the programs taught in the Data Analytics course is R Studio. This program provides visuals based on data sheets. A few visuals we generated were histograms and decision trees. Sentiment analysis can be used to generate decision trees as once the data is collected, it can be broken down into different decisions and the percentage of selecting that decision. 

One example of how text mining and sentiment analysis is applied in practice is with R Studio. The data is collected on an excel sheet through text mining. Then it is imported into R Studio. Thereafter, a script is written to generate a decision tree. For example, the decision is what genre is preferred most when selecting to watch a movie.  All the genres are listed on the excel sheet along with the data of which genres consumers selected. Then, R Studio generates a decision tree based on the written script with how likely consumers are to select genres whether it be based on previous reviews and ratings. This is all generated because of the sentiment analysis on generated from the reviews and rating collected through text mining.

Works Cited

Bannister, Kristian. “Sentiment Analysis: How Does It Work? Why Should We Use It?” Brandwatch. Brandwatch Inc, 16 Aug. 2016. Web. 27 Apr. 2017. <https://www.brandwatch.com/blog/understanding-sentiment-analysis/>.

Rouse, Margaret. “What Is Text Mining (text Analytics)? – Definition from WhatIs.com.” SearchBusinessAnalytics. TechTarget, n.d. Web. 27 Apr. 2017. <http://searchbusinessanalytics.techtarget.com/definition/text-mining>.

“Sentiment Analysis.” Sentiment Analysis | Lexalytics. Lexalytics Inc, n.d. Web. 27 Apr. 2017. <https://www.lexalytics.com/technology/sentiment>.

 

 

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