Classification of Student’s E-Learning Experiences’ in SocialMedia via Text Mining
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 3
Abstract
Abstract : In today’s world, social media is used every individual for expressing their feelings, opinion,experiences’ and emotions. Applying data mining on all these emotions expressed in posts, comments and likescalled as social media data. In Existing system, only the prominent themes are identified with relatively largenumber of tweets in the data. There are a variety of other issues hidden in the “others” theme. Several of theseissues may be of great interest to education researchers and practitioners. The fact that the most relevant datawhich are found on engineering students’ learning experiences involve complaints, issues, and problems doesnot mean there are no positive sides in students’ learning experiences’. This may entail that social media serveas a good venue for students to utter negative emotions and seek social support. In proposed work, New LabelGood Things introduce and using the probability, the common keywords are considered for this label. Doingthis, the proposed system is identifying and classifying the e-learning problems faced by student to improve theireducation quality with respect to their good and positive comments. Naive Bayes multilabel classifier is used for classification of experiences' by finding the probability of words in tweet for each category probability of eachlabel contains how many users. Finally the tweets with new Label will be compared with the rest of the tweetswith existing Labels
Authors and Affiliations
Ms. Priyanka Patel , Ms. Khushali Mistry
Keywords
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