Descriptive, dynamic and hybrid classification algorithm to classify engineering students’ sentiments

Abstract

The social networking sites have brought a novel horizon for students to share their views about the learning process. Such casually shared information has a great venue in decision making. However, the growing scale of data needs automatic classification method. Sentiment analysis is one of the automated methods to classify huge data. The existing sentiment analysis methods are extremely used to classify online reviews to provide business intelligence. However, they are not useful to draw conclusions on education system as they classify the sentiments into merely three pre-set categories: positive, negative and neutral. Moreover, classifying students’ sentiments into positive or negative category does not provide concealed vision into their problems and perks. Unlike traditional predictive algorithms, our Hybrid Classification Algorithm (HCA), makes the sentiment analysis process descriptive. The descriptive process helps future students and education system in decision making. In this paper, we present the performance evaluation of HCA under four datasets collected by different methods, different time spans, different data dimensions and different vocabulary and grammar. The experimental results show that the hybrid, dynamic and descriptive algorithm potentially outperforms the traditional static and predictive methods.