<![CDATA[TikTok has become a widely used social media platform where users actively express opinions through comment features. This study aims to classify the emotions contained in TikTok user comments on the Indonesian animated film Jumbo using the Naive Bayes Classifier method. The dataset consisted of 1,341 comments collected from the official Visinema Pictures account using the Apify Web Scraper. The collected data were processed through several preprocessing stages, including case folding, tokenization, normalization, stopword removal, and stemming using the Sastrawi library. Emotion labeling was performed based on the Indonesian NRC EmoLex lexicon by categorizing comments into three emotional classes: angry, happy, and sad. Feature extraction was conducted using the TF-IDF weighting method to generate relevant text representations and identify dominant terms in each emotional category. The dataset was divided into 80% training data and 20% testing data to evaluate the model performance. The experimental results show that the Naive Bayes model achieved an accuracy of 78.81%. The emotion distribution indicates that anger was the most dominant class with 904 comments, followed by happy with 415 comments, and sad with 22 comments. The model demonstrated the best performance in the anger class, achieving 100% recall, 75% precision, and an F1-score of 85.71%. However, the classification performance for minority classes, particularly happy and sad, still requires improvement. This research contributes to the development of text mining-based emotion analysis and provides insights into audience emotional responses that may support film evaluation and marketing strategies.]]>
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