<![CDATA[This research explores sentiment analysis on user reviews of the game Mobile Legends: Bang Bang using the Naïve Bayes method. With the rapid growth in user numbers, the reviews received reflect a diverse range of positive, negative, and neutral sentiments. One of the main challenges is the data imbalance among the three sentiments, which can affect the model's accuracy. Data was collected through scraping techniques from the Google Play Store, followed by preprocessing to enhance data quality. The analysis results show that the Naïve Bayes model achieved an accuracy of 75.28%, demonstrating good performance in identifying negative reviews, although there is still room for improvement in the positive and neutral categories. These findings are expected to provide valuable insights for game developers in understanding user experiences and improving application features based on sentiment analysis. ]]>
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