  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.
