User Sentiment Analysis of the ChatGPT Application on Google Play Store using the Naïve Bayes Algorithm
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Abstract
User reviews on the Google Play Store reflect public perceptions and satisfaction toward the ChatGPT AI application. This study aims to analyze user sentiment using the Multinomial Naïve Bayes algorithm. The research follows the stages of the Knowledge Discovery in Database (KDD) process, including data selection, preprocessing, transformation, data mining, and evaluation. A total of 1,000 reviews were classified into three sentiment categories—positive, negative, and neutral—and tested using data split ratios of 50:50, 70:30, and 80:20. The best performance was achieved with the 80:20 ratio, producing an accuracy of 94%, precision of 96%, and recall of 99% for positive sentiment. These findings indicate that the Naïve Bayes algorithm can effectively classify user sentiment and provide valuable insights into public perceptions of the quality and performance of the ChatGPT application.
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