Sentiment Analysis in Valorant Game Review Using Information Gain
Keywords:
Sentiment analysis, Information Gain, Game, Multinomial naive Bayes, support vector machineAbstract
Video games are one of the entertainments that have developed rapidly compared to other forms of entertainment such as movies or books. because of the rapid development that makes many games need to be reviewed to determine whether they game is worth playing or not. In general, reviews are divided into 2 opinions, namely negative reviews, and positive reviews, but some reviews cannot be included in the positive or negative category by the computer because the review has ambiguous words. Therefore we need a method that can help the computer in determining the category of the review, one of the methods used is the sentiment analysis method. In this study, the authors researched the valorant game review dataset using the information gain method using several classifiers, namely SVM, and Multinomial Naive Bayes and produced the greatest accuracy for the Multinomial Naive Bayes classifier method with an accuracy of 85.90%.
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