ANALISA SENTIMEN DATA ULASAN PADA GOOGLE PLAY DENGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE

Abstract

Reviews on the Google Play website are an application's user ratings, which contain users' ratings and comments. Review data describes user sentiment towards the application according to the ratings and comments are given. In practice, there is often a discrepancy between the rating and the comments given, resulting in a biased sentiment, so it is necessary to analyze the review to find out the sentiment contained therein. The Naïve Bayes method and the Support Vector Machine can be one of the methods that are often used to carry out sentiment analysis, because they have a good level of accuracy in the sentiment analysis process. In collecting data from the Google Play site using the Web Scraping technique with the google- play-scraper package from Python. Reviews that are successfully scraped then go through the preprocessing stage so that the data set is more structured. In the next stage, the data set is labeled based on the rating, and given a weight using TF-IDF. After classifying using the Naïve Bayes and Support Vector Machine methods, then evaluating using the confusion matrix, and validating using K-Fold Cross Validation. Research results using the Naïve Bayes method and Support Vector Machine for sentiment analysis on the Google Play website, the Naïve Bayes method produces 87.82% accuracy, 58.90% precision, 60.08% recall, while the Support Vector Machine method produces 90% accuracy .01% , precision 61.89%, recall 60.18%.