A Hybrid Classification Algorithm to Classify Engineering Students'   Problems and Perks
  The social networking sites have brought a new horizon for expressing views and opinions of individuals. Moreover, they provide medium to students to share their sentiments including struggles and joy during the learning process. Such informal information has a great venue for decision making. The large and growing scale of information needs automatic classification techniques. Sentiment analysis is one of the automated techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful to draw decisions in education system since they classify the sentiments into merely three preset categories: positive, negative and neutral. Moreover, classifying the students sentiments into positive or negative category does not provide deeper insight into their problems and perks. In this paper, we propose a novel Hybrid Classification Algorithm to classify engineering students sentiments. Unlike traditional predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process descriptive. Moreover, it classifies engineering students perks in addition to problems into several categories to help future students and education system in decision making. 
Mitali Desai and Mayuri A. Mehta
cs.CY