<![CDATA[This study investigates public sentiment toward online gambling on TikTok social media and evaluates the performance of two machine learning algorithms, Multinomial Naïve Bayes and Support Vector Machine (SVM), for sentiment classification. The increasing spread of online gambling content on social media platforms has raised concerns regarding its social impact and the need to understand public responses toward this phenomenon. Therefore, this research aims to analyze the sentiment expressed in TikTok comments and to compare the effectiveness of Naïve Bayes and SVM in classifying sentiments into positive, negative, and neutral categories.The dataset consists of 99,065 TikTok comments obtained from Kaggle. The research process includes data collection, text preprocessing, feature extraction using TF–IDF, model training, and evaluation using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. The experimental results show that the dataset is highly imbalanced, with neutral sentiment dominating the distribution.The evaluation results indicate that the Support Vector Machine model performs better than Multinomial Naïve Bayes. SVM achieved an accuracy of 98.74%, outperforming Naïve Bayes which obtained 94.72%. In addition, SVM also demonstrates higher macro-averaged precision, recall, and F1-score. These findings indicate that SVM provides more robust performance for sentiment classification in large-scale social media data and can support data-driven monitoring of public opinion regarding online gambling on social media platforms.]]>
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