Improving the Accuracy of Multinomial Naïve-Bayes Algorithm with Adaptive Boosting Using Information Gain for Classification of Movie Reviews Sentiment Analysis

Abstract

Movie is a means of delivering information as well as entertainment that can be enjoyed by all people through various platforms such as the internet, cinema, and television. Sentiment analysis is needed to analyze positive and negative comments from movie lovers, these comments come from many circles and from various sources, one of which is IMDb (Internet Movie Database). The naïve-Bayes multinomial classification algorithm has been proposed and used by many researchers in the case of sentiment analysis. The ensemble adaptive boosting algorithm is used as a boosting algorithm to improve accuracy in naïve-Bayes and information gain multinomial classification models. The accuracy test on the model is carried out using the python programming language. The accuracy results obtained when applying the naïve-Bayes multinomial classification algorithm is 84.82%, then an accuracy of 85.24% is obtained when implementing the informationgain feature selection on the naïve-Bayes multinomial classification algorithm. The highest accuracy result of 87.87% was obtained when implementing the naïve-Bayes multinomial classification algorithm with adaptive boosting and information gain selection features.