An Empirical Study On Sentiment Polarity Classification Of Book Reviews

Abstract

Sentiment polarity classification deals with automatic classification of text in sentiment polarity categories. While in most of proposed approaches for polarity classification, a dictionary containing polarity-based terms is considered. Such dictionaries are not readily available. We have adopted a machine learning based approach where classifiers are trained over a self-collected corpus of book reviews, annotated with sentimental categories. In this paper, we have presented our investigation of performance evaluation of machine learning classifiers. Five classifiers are evaluated including naïve Bayes, k-nearest neighborer, decision tree and support vector machine. Naïve Bayes has shown us best results.