<![CDATA[The rapid growth of social media platforms, especially Twitter (now rebranded as X), has made it a rich source for public opinion mining. This study focuses on analyzing public sentiment toward the animated “film Jumbo" by applying the Naïve Bayes Classifier algorithm to Twitter data. The research involves collecting tweets related to the film, preprocessing the data by removing noise such as stopwords, user mentions, and special characters, and then classifying sentiments into positive, negative, and neutral categories. The Naïve Bayes algorithm was chosen due to its effectiveness and efficiency in text classification tasks such as sentiment analysis. The classifier is trained on a labeled dataset and evaluated through performance metrics including accuracy, precision, recall, and F1-score. The results demonstrate that the Naïve Bayes Classifier can effectively capture public sentiment on social media, providing insights into audience reception of the “film Jumbo." Moreover, this research highlights the importance of integrating machine learning techniques with social media analytics for real-time sentiment monitoring. The model achieved an accuracy of 77% in classifying sentiments. Analysis results indicate that positive sentiment dominates public opinion, with a precision of 88% and recall of 83%, followed by negative sentiment with precision and recall of 75% and 71%, respectively, and neutral sentiment with precision of 40% and recall of 57%. These findings offer valuable insights for the film industry to better understand audience perceptions and make informed decisions.]]>
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