<![CDATA[This study conducts sentiment analysis on user reviews of PUBG (PlayerUnknown's Battlegrounds) to understand players' perceptions of their gaming experience. The main issue addressed is the need for a deeper understanding of player satisfaction and dissatisfaction through available review data. The objective of this research is to identify sentiment tendencies—positive, neutral, or negative—and the specific aspects of the game that receive the most user feedback. The methodology combines a lexicon-based sentiment analysis approach with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. The lexicon-based technique is used to classify words with positive or negative connotations, while TF-IDF highlights the most influential terms in the user review corpus. Results from various testing scenarios show that this combined method improves sentiment classification accuracy. The analysis reveals that the majority of reviews express positive sentiment, with a smaller portion being neutral or negative. Furthermore, the approach successfully identifies the most praised and criticized features of the game. These findings offer valuable insights for game developers to evaluate and improve game elements that directly impact player satisfaction and retention]]>
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