Sentiment Analysis on App Reviews Using Support Vector Machine and Naïve Bayes Classification

Abstract

A review is an assessment given by someone based on certain aspects, such as the delivery of stories, pictures, effects, or visuals. Users can provide reviews which help the company know the quality of the application. However, reviews cannot be used as a reference for rating, because there are still users who provide reviews that are irrelevant to the rating given. This study aims to carry out sentiment analysis in order to classify the application user review data. The sentiment classification process begins with collecting and labeling 700 data. The data then goes through a text preprocessing, word weighting with TF-IDF, and classification using the Support Vector Machine and Naïve Bayes Classifier. The results produce the highest accuracy in the comparison of training and test data of 90%:10%. Support Vector Machine algorithm is capable of providing high accuracy with RBF kernel, γ=1, and C=10. The results obtained using 10-fold cross validation give an accuracy value of 92.86%, a precision value of 92.88%, a recall value of 92.88%, a specificity value of 94.73%, and f-measure of 92.76%. Naïve Bayes Classifier method is able to provide high accuracy by using Multinomial Naïve Bayes Classifier. The results obtained using 10-fold cross validation give an accuracy value of 92.54%, a precision value of 92.55%, a recall value of 92.51%, a specificity value of 93.9%, and f-measure of 92.44%. Based from the result, it can be concluded that the classification using the Support Vector Machine is superior to the Naive Bayes Classification.