<![CDATA[interactions, one of which is TikTok. The TikTok platform has become a global phenomenon favored by many, especially the younger generation. As the number of users increases, reviews on digital platforms such as the Google Play Store become an important source for understanding users' perceptions of the application. Therefore, a deep understanding of user sentiment toward TikTok is essential for better app development and effective marketing strategies. To analyze TikTok user sentiment, this study employs two well-established computational methods: Support Vector Machine (SVM) and Naïve Bayes. These methods are used to classify user reviews into positive or negative sentiment categories. The approach involves several stages, including data collection, data preprocessing, data splitting, sentiment classification, and model evaluation. The study shows that the SVM model achieved an accuracy of 88.76% with an AUC of 92.61%, outperforming Naïve Bayes, which achieved an accuracy of 84.27% and an AUC of 92.57%. In the positive sentiment category, SVM recorded a precision of 90.74% and a recall of 95.15%, while Naïve Bayes yielded a precision of 83.61% and an almost perfect recall of 99.03%. For negative sentiment, SVM showed a precision of 80.39% and recall of 67.21%, whereas Naïve Bayes had a higher precision of 91.30% but a lower recall of 34.43%, with a lower F1-score of 50%.]]>
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