Sentiment Analysis on ChatGPT App Reviews on Google Play Store Using Random Forest Algorithm, Support Vector Machine and Naïve Bayes

Abstract

This study aims to conduct a Sentiment Analysis on ChatGPT App reviews on the Google Play Store using three classification methods: Random Forest Algorithm, Support Vector Machine (SVM), and Naïve Bayes. The main purpose of this study is to detail and understand user sentiment towards the application. From a total of 2652 review data regarding ChatGPT performance from July 28, 2023, to January 28, 2024, the results were 2326 (87.71%) positive reviews and 326 (12.29%) negative reviews, which means that the public is more dominant in responding positively to the use of ChatGPT based on Google Play Store ratings. In this study, researchers used the f1-score to see which method works best because the data has an imbalance of data, so the f1-score is the best way to provide information about how well the model handles minority classes. Through the classification of three different algorithms with testing data taken from 796 (30%) from a total of 2652 rating reviews, it was found that Random Forest got an f1-score of 90% with positive correct data as much as 87.43% and negative accurate data as much as 0.75%, Support Vector Machine got an f1-score value of 90% with positive valid data as much as 86.80% and negative correct data as much as 0.13%, and Naïve Bayes received an f1-score of 87% with positive, accurate data of 88.06% and negative valid data of 0.12%. Therefore, it can be concluded from this study that users who experienced the development of the ChatGPT application felt a more striking positive impact, and the Support Vector Machine and Random Forest methods became the most effective methods in this study, proven by the highest f1-score value.