Published: 2024-04-01

Analisis Sentimen Film Dirty Vote Menggunakan BERT (Bidirectional Encoder Representations from Transformers)

DOI: 10.35870/jtik.v8i2.1580

Diah Fatma Sjoraida, Bucky Wibawa Karya Guna, Dudi Yudhakusuma
  • Diah Fatma Sjoraida: Padjadjaran University ROR
  • Bucky Wibawa Karya Guna: Sekolah Tinggi Musik Bandung
  • Dudi Yudhakusuma: Universitas Langlangbuana ROR
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Abstract

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.

Keywords

Sentiment Analysis; Dirty Vote Film; BERT (Bidirectional Encoder Representations from Transformers); Audience Response

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How to Cite

Sjoraida, D. F., Guna, B. W. K., & Yudhakusuma, D. (2024). Analisis Sentimen Film Dirty Vote Menggunakan BERT (Bidirectional Encoder Representations from Transformers). Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(2), 393-404. https://doi.org/10.35870/jtik.v8i2.1580
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 8 No. 2 (2024)

  • Section: Computer & Communication Science

  • Published: 2024-04-01

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Author Biographies
Diah Fatma Sjoraida, Padjadjaran University
Google Scholar ResearchGate ORCID

Program Studi Ilmu Komunikasi, Magister Ilmu Komunikasi, Universitas Padjadjaran, Kabupaten Sumedang, Provinsi Jawa Barat, Indonesia

Bucky Wibawa Karya Guna, , Sekolah Tinggi Musik Bandung
Google Scholar ResearchGate ORCID

Program Studi Seni Musik, Sekolah Tinggi Musik Bandung, Kota Bandung, Provinsi Jawa Barat, Indonesia

Dudi Yudhakusuma, Universitas Langlangbuana
Google Scholar ResearchGate ORCID

Program Studi Ilmu Komunikasi, Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Langlangbuana, Kota Bandung, Provinsi Jawa Barat, Indonesia

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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • 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.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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