Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting

Abstract

Sentiment analysis problems have attracted the attention of researchers. Sentiment analysis is a process that aims to determine the sentiment polarity of text. Nowadays, sentiment from product reviews has become a piece of important information for producers and potential customers. This paper conducted a sentiment analysis classification on a movie review from the IMDb site. In the classification analysis, the sentiment of movie reviews used the multinomial naïve Bayes algorithm. Adaboost was applied to boosting the accuracy of multinomial naïve Bayes. Feature selection is used to reduce the number of features and irrelevant features. The chi-square feature selection used was employed in the current study. The accuracy obtained in movie review sentiment analysis classification using the multinomial naïve Bayes algorithm is 81.39%. Meanwhile, the accuracy of the multinomial naïve Bayes algorithm by applying chi-square is 85.37%. The final result of multinomial naïve Bayes algorithm accuracy by applying AdaBoost and chi-square feature selection is 87.74%.