An Analysis of Students Opinions on Social Networking Site for Understanding Student’s Learning Practices

  Prof. Mangesh Balpande,  Prof. Deven Ketkar,  Prof. Mahesh Patil

Recent year’s rapid growth in internet use, social networking sites become important medium of communication. By using social networking sites such as Facebook, twitter, LinkedIn etc., millions of messages are exchanged. Different users share their personal opinions or views about various issues. They also discuss several current hot trending topics on Twitter and Facebook. Many people including youngsters, businessmen, sports players and film industry people use different social networking sites for sharing their views on different trends. By using this information that we generate from different posts, making it an important base for tracking and analyzing sentimentation of students. This information is useful in decision making and opinion mining. In this work, we have moved one step further to interpret sentiment variations using twitter tweets. We observed that emerging topics (named foreground topics) within the sentiment variation periods are highly related to the genuine reasons behind the variations. We select the most representative tweets for foreground topics and develop another generative model called Reason Candidate and Background LDA (RCB-LDA) for ranking them with respect to their “popularity” within the variation period. Opinion mining also known as sentiment analysis plays an important role in determining the sentiments involved in various content. For example, if anyone wants to buy a new car, a buyer will always check reviews. Based on that reviews he/she will take a decision. These kind of decisions are based on others experiences. Student sentiment analysis is currently a very significant trend in the area education system. Many universities can make use of these data for taking opinion on their services that they provide to student. Student sentiment analysis is important task in the area of natural language processing. Natural language processing involves giving artificial intelligence to computers and is concerned with promoting an understanding of human languages for machines’ use. Student sentiment analysis extracts opinions, sentiments and emotions from text and analyze them this information is very useful for governments, businesses and individuals. Hence, need arises to develop an intelligent system which mine such huge content and classify them into Negative, Positive, Neutral type. Student sentiment analysis is the automated mining of opinions, attitudes, emotions from text and other posts, and database sources through Natural Language Processing (NLP).

Keywords- Student sentiment Analysis, Public sentimental Social Sites, Twitter, Emerging topic mining.

Unique Identification Number - IJEDR1703178
Page Number(s) - 1225-1230
Pubished in - Volume 5 | Issue 3 | September 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Prof. Mangesh Balpande,  Prof. Deven Ketkar,  Prof. Mahesh Patil,   "An Analysis of Students Opinions on Social Networking Site for Understanding Student’s Learning Practices", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 3, pp.1225-1230, September 2017, Available at :http://www.ijedr.org/papers/IJEDR1703178.pdf