This research aims to conduct sentiment analysis on reviews of the film "Dirty Vote" from various sources, including social media, film review websites, and online forums, using a fine-tuned BERT model. The approach involves data collection, data preprocessing, BERT model refinement, and model performance evaluation. The results show that the BERT model achieves high performance with accuracy, precision, recall, and F1-score exceeding 0.8 on the validation dataset. Sentiment analysis from various sources reveals variations in public opinion towards "Dirty Vote," with significant differences in sentiment expressed via social media (Twitter and Facebook) compared to reviews from dedicated websites or online forums. The analysis also highlights preferences for certain aspects of the film, such as visual effects and music, which received the highest ratings, while the cast and director received lower ratings. This information can be used by filmmakers to improve unsatisfactory aspects in subsequent film production. The study contributes to understanding public perception and provides valuable insights for filmmakers and policymakers.This research aims to conduct sentiment analysis on reviews of the film "Dirty Vote" from various sources, including social media, film review websites, and online forums, using a fine-tuned BERT model. The approach involves data collection, data preprocessing, BERT model refinement, and model performance evaluation. The results show that the BERT model achieves high performance with accuracy, precision, recall, and F1-score exceeding 0.8 on the validation dataset. Sentiment analysis from various sources reveals variations in public opinion towards "Dirty Vote," with significant differences in sentiment expressed via social media (Twitter and Facebook) compared to reviews from dedicated websites or online forums. The analysis also highlights preferences for certain aspects of the film, such as visual effects and music, which received the highest ratings, while the cast and director received lower ratings. This information can be used by filmmakers to improve unsatisfactory aspects in subsequent film production. The study contributes to understanding public perception and provides valuable insights for filmmakers and policymakers.