SENTIMENT CLASSIFICATION OF MOVIE REVIEWS BY SUPERVISED MACHINE LEARNING APPROACHES

Large volumes of data are available in the web. The discussion forum, review sites, blogs and news corpora are some of the opinion rich resources. The opinions obtained from those can be classified and used for gathering online customer’s preferences. Techniques are being applied to design a system that identifies and classify opinions spread largely in the internet. Few different problems such as sentiment classification, feature based classification and handling negotiations are predominating this research community. This paper studies online movie reviews using sentiment analysising approaches. In this study, sentiment classification techniques wereapplied to movie reviews. Specifically, we compared three supervised machine learning approaches SVM, Navie Bayes and kNN for Sentiment Classification of Reviews. Empirical results states that SVM approachoutperformed the Navie Bayes and kNN approaches, and the training dataset had a large number of reviews, SVM approach reached accuracies of atleast 80%.