Optimized support vector machine for sentiment analysis of game reviews

Abstract

The rapid development of games has made game categories diverse, so there are many opinions about games that have been released. Sentiment analysis on game reviews is needed to attract potential players. Sentiment analysis is carried out using the support vector machine (SVM) and particle swarm optimization (PSO) algorithms. SVM training was conducted with a linear kernel, the ‘C’ value parameter was 10 resulting in an accuracy value of 97.28%. The SVM algorithm optimized using the PSO method produces an accuracy of 97.61% using the parameters c1 is 0.2, c2 is 0.5 and w is 0.6. Based on these results, sentiment analysis using PSO-based SVM optimization has been successfully carried out with an increase in accuracy of 0.33%. This game review has a sentiment value from neutral to positive so this game can be recommended to other players.