IMPROVEMENT OF NAIVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS ON GOOGLE PLAY STORE

Abstract

Reviews of the shopee application on the google play store are included in useful information if processed properly. Old or new users can analyze app reviews to get information that can be used to evaluate services. The activity of analyzing application reviews is not enough just to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. A sentiment analysis system is a system used to automatically analyze a review to obtain information including sentiment information that is part of an online review. The data is classified using Naive Bayes. A total of 1,000 shopee app user reviews were collected to form the sample dataset. The purpose of this study is to determine the sentiment analysis of shopee application reviews in the Google Play Store using the Naive Bayes algorithm. The stages of this research include, data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Case folding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 81%, this result shows that shopee application reviews tend to be negative.