Analisis Sentimen Film Dirty Vote Menggunakan BERT (Bidirectional Encoder Representations from Transformers)
DOI:
https://doi.org/10.35870/jtik.v8i2.1580Keywords:
Sentiment Analysis, Dirty Vote Film, BERT (Bidirectional Encoder Representations from Transformers), Audience ResponseAbstract
This research aims to conduct sentiment analysis on reviews of the film "Dirty Vote" from various sources, such as social media, film review websites, and online forums, using a fine-tuned BERT model. This approach includes review data collection, data pre-processing, BERT model refinement, and model performance evaluation. The research results show that the BERT model achieves a high level of performance with accuracy, precision, recall, and F1-score exceeding the threshold of 0.8 on the validation dataset. Sentiment analysis from various sources revealed variations in public opinion toward the film “Dirty Vote,” with significant differences in sentiment expressed via social media such as Twitter and Facebook compared to reviews from dedicated websites or online forums. In addition, discussion analysis of sentiment findings revealed people's preferences for certain aspects of films, such as visual effects and music. Sentiment analysis findings revealed that visual effects and music received the highest ratings from the public, while the cast and director received lower ratings. This information can be used by filmmakers to improve unsatisfactory aspects in subsequent film production.
Downloads
References
Wardhana, S. R., & Purwitasari, D. (2019). Klasifikasi multi class pada analisis sentimen opini pengguna aplikasi mobile untuk evaluasi faktor usability. INTEGER: Journal of Information Technology, 4(1). DOI: https://doi.org/10.31284/j.integer.2019.v4i1.474
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. DOI: https://doi.org/10.48550/arXiv.1810.04805
Kusnadi, R., Yusuf, Y., Andriantony, A., Yaputra, R. A., & Caintan, M. (2021). Analisis sentimen terhadap game genshin impact menggunakan bert. Rabit: Jurnal Teknologi Dan Sistem Informasi Univrab, 6(2), 122-129. DOI: https://doi.org/10.36341/rabit.v6i2.1765
Zhang, L., Fan, H., Peng, C., Rao, G., & Cong, Q. (2020). Sentiment analysis methods for HPV vaccines related tweets based on transfer learning. Healthcare, 8(3), 307. https://doi.org/10.3390/healthcare8030307.
Wang, T., Ke, L., Chow, K., & Zhu, Q. (2020). Covid-19 sensing: negative sentiment analysis on social media in China via BERT model. IEEE Access, 8, 138162-138169. DOI: https://doi.org/10.1109/access.2020.3012595
Kowsher, M., Sami, A. A., Prottasha, N. J., Arefin, M. S., Dhar, P. K., & Koshiba, T. (2022). Bangla-bert: transformer-based efficient model for transfer learning and language understanding. IEEE Access, 10, 91855-91870. DOI: https://doi.org/10.1109/access.2022.3197662
Li, H., Ma, Y., Ma, Z., & Zhu, H. (2021). Weibo text sentiment analysis based on BERT and deep learning. Applied Sciences, 11(22), 10774. DOI: https://doi.org/10.3390/app112210774
Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., ... & Baz, M. (2022). Transfer learning for sentiment analysis using BERT based supervised fine-tuning. Sensors, 22(11), 4157. DOI: https://doi.org/10.3390/s22114157
Wu, Z., & Ong, D. C. (2021). Context-guided BERT for targeted aspect-based sentiment analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14094-14102. DOI: https://doi.org/10.1609/aaai.v35i16.17659
Alaparthi, S., & Mishra, M. (2021). BERT: A sentiment analysis odyssey. Journal of Marketing Analytics, 9(2), 118-126. DOI: https://doi.org/10.1057/s41270-021-00109-8
Fimoza, D., Amalia, A., & Harumy, T. H. F. (2021, November). Sentiment analysis for movie review in Bahasa Indonesia using BERT. In 2021 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA) (pp. 27-34). IEEE. DOI: https://doi.org/10.1109/DATABIA53375.2021.9650096
Man, R., & Lin, K. (2021, April). Sentiment analysis algorithm based on bert and convolutional neural network. In 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 769-772). IEEE. DOI: https://doi.org/10.1109/IPEC51340.2021.9421110
Maltoudoglou, L., Paisios, A., & Papadopoulos, H. (2020, August). BERT-based conformal predictor for sentiment analysis. In Conformal and Probabilistic Prediction and Applications (pp. 269-284). PMLR.
Ansar, W., Goswami, S., Chakrabarti, A., & Chakraborty, B. (2021). An efficient methodology for aspect-based sentiment analysis using BERT through refined aspect extraction. Journal of Intelligent & Fuzzy Systems, 40(5), 9627-9644.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Diah Fatma Sjoraida, Bucky Wibawa Karya Guna, Dudi Yudhakusuma

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
