Analyzing Social Media Data to Explore Students’ Academic Experiences

Abstract

The casual conversational style used by the students on any front stage environment can educate extensively about their learning process. The collection of data from such an open environment can bring out many important and unknown factors about students" behaviour, their opinions, their feelings their concerns pertaining to their educational system. The inspection of such data can be said to be very provocative. The reflection of students" feelings over the social network, however, has to undergo the human eye to get properly interpreted, which is possible but upto a certain extent, as a result of ever-growing data. In this paper, problems of engineering students have been considered. This has been worked upon by analysing engineering students" tweets from the hashtag #enggproblems on Twitter. Analysis was carried out over 15,000 tweets. These problems were related to heavy study load, negative emotions, sleep problems, lack of social engagement, diversity issues etc. A multi-label classifier was executed to classify and categorize tweets. This technique can dig up into the casual conversations of students and educate about the factors that affect the learning process of students. General Terms Multi-label classification using naïve bayes classifier.