Understanding Students’ Learning Experiences By Mining Social Media Data
Garima Chanana1, V.Vijaya Kumar2, M.Geetha3
1Garima Chanana, VIT University Chennai, (Tamil Nadu), India.
2Dr.V.VijayaKumar, Associate Dean, VIT University Chennai, (Tamil Nadu), India.
3M.Geetha, Assistant Professor, VIT University, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1392-1398 | Volume-7 Issue-6, March 2019 | Retrieval Number: F3014037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Conversations of students on social networking sites like twitter, facebook throw light on education experiences like emotions, concerns. Twitter is a micro-blog with each tweet within 100-150 words so we can understand emotions of candidates. Most tweets are related to emotions, which tweets fall under which emotion. In this paper we are focusing to develop a model which predicts student’s emotion and understand their feelings, opinions related to their educational experiences. Few labels which we have used for fetching the tweets related to students are exams, results, engineering. Main phases in this application are text cleaning, processing, validation and prediction. In pre-processing /cleaning phase stop-words removal, stripping white-space, removing punctuation. In processing phase, document term matrix, creating corpus and applying supervised learning paradigms on training data. We validated the accuracy of model using 5-fold cross validation in validation phase. On the basis of training data, predicted the label of tweets in test data.
Keywords: Social media, College, Twitter, Tweets , Text mining ,Supervised learning , Machine learning , Visualization ,Preprocessing , Classification , SVM , Sentiment analysis.
Scope of the Article: Classification