<![CDATA[Subway Surf, an endless run arcade game, offers a dynamic experience and online competition that successfully attracts players. Although the majority of reviews on the Play Store were positive, some users expressed dissatisfaction with some aspects. This study uses the Naïve Bayes method to analyze the sentiment of reviews, with structured steps such as data collection, preprocessing, data sharing, model application, evaluation, and result interpretation. The predominance of positive sentiment in Figure 3 reflects player satisfaction, while negative sentiment highlights game progress issues and technical constraints. The Naïve Bayes classification model gave good results, with 86% and 79% precision for negative and positive classification, 78% and 86% recall, and an F1-score of 82%. The total accuracy reached 82%, indicating good predictive ability on the test dataset. Recommendations for future research include more in-depth analysis for user experience improvements, ensuring Subway Surf continues to maintain its appeal amidst growing player growth]]>
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