Rating generation of video games using sentiment analysis and contextual polarity from microblog

Abstract

In these days people tend to check reviews and ratings of video games before spending money and time for a game. This paper proposes a new model of sentiment analysis for video game's reviews. In the proposed model, video game's rating will be generated by doing sentiment analysis on public opinion data from Micro-blog site, Twitter. To segregate users' sentiment Naïve Bayes, Support Vector Machine, Logistic Regression and Stochastic Gradient Descent machine learning algorithms were used. This algorithms themselves were trained and tested on the Amazon game review dataset before doing sentiment analysis on Twitter Data. Furthermore, customized classifiers model was implemented which acted as voting classifiers to determine contextual polarity. This voting classifier takes results of other algorithms into account and select the best one which can obtain the most number of votes. Before implementing the classifiers, data pre pro-processing had been performed for accurate sentiment analysis. This process ensures higher accuracy for generating review ratings and distinguishing users' sentiment. Finally, algorithm's accuracy results are analyzed in vivid details. Analysis showed that our proposed technique outperforms others.