Sentiment Analysis Of Bali's Rapid Tourism Growth Using IndoBERT With InSet Lexicon Automatic Labelling - Ni Ketut Artini Artalia; I Made Dendi Maysanjaya; Putu Hendra Suputra (Universitas Pendidikan Ganesha, 2026)
Bali’s rapid tourism growth has generated diverse public opinions, particularly on TikTok, where comment sections reflect debates and societal concerns. The absence of an automated sentiment monitoring system may delay policy responses and potentially intensify social conflict. Therefore, sentiment analysis is required to understand public perception comprehensively. This study conducts sentiment analysis on TikTok comments related to Bali’s tourism growth using a transformer-based approach. IndoBERT, a pre-trained model for Indonesian-language processing, is employed due to its superior performance compared to traditional machine learning methods. Sentiment labelling is performed using the Indonesian Sentiment Lexicon (InSet Lexicon), which provides an efficient and consistent automatic labelling alternative to manual annotation. Model performance is evaluated through two experimental scenarios: IndoBERT with InSet Lexicon automatic labelling, and IndoBERT combined with InSet Lexicon and Random Over Sampling (ROS). The results show that applying ROS significantly improves classification performance by addressing class imbalance, resulting in higher accuracy, precision, recall, and F1-score. The analysis indicates that public sentiment toward Bali’s tourism growth is predominantly neutral, suggesting that most user express opinions in an informative and descriptive manner rather than strong emotional responses. Overall, the integration of IndoBERT, InSet Lexicon automatic labelling, and Random Over Sampling is proven to be an effective approach for Indonesian sentiment analysis, particularly in examining socially informative issues such as tourism development in Bali.