Chi-Square Feature Selection Effect On Naive Bayes Classifier Algorithm Performance For Sentiment Analysis Document

Abstract

The main problem in using a sentiment analysis algorithm Naïve Bayes is sensitivity to the selection of features. There exist Chi-Square feature selections to eliminate features that are not very influential. This study aimed to determine the effect of Chi-Square feature selection on the performance Naïve Bayes algorithm in analyzing sentiment documents. Data were taken from Corpus v1.0 Indonesian Movie Review 700 training data and 30 test data. Testing was done by analyzing sentiment documents with and without a Chi-Square feature selection. The evaluated subsequently by the method of accuracy, precision, and recall. The result from the analysis of sentiment without feature selection obtained 73.33% accuracy, precision 100.00%, 65.21% recall. While the Chi-Square feature selection (significance level a 0.1) obtained 93.33% accuracy results, Precision 93.33%, and 93.33% recall. From these results, it can be seen that the selection of Chi-Square features affects performance Naïve Bayes algorithm in analyzing sentiment documents.