Analisis Sentimen Film Dirty Vote Menggunakan BERT (Bidirectional Encoder Representations from Transformers)
Article Sidebar
Published:
Apr 1, 2024
Keywords:
Sentiment Analysis, Dirty Vote Film, BERT (Bidirectional Encoder Representations from Transformers), Audience Response
Scite_ AI Article:
Connected Papers:
Statistics:
Share Article:
Main Article Content
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.
Downloads
Download data is not yet available.
Article Details
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
Section
Computer & Communication Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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.
Lehečka, J., Švec, J., Ircing, P., & Šmídl, L. (2020, September). Bert-based sentiment analysis using distillation. In International Conference on Statistical Language and Speech Processing (pp. 58-70). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-59430-5_5
Selvakumar, B., & Lakshmanan, B. (2022). Sentimental analysis on user’s reviews using BERT. Materials Today: Proceedings, 62, 4931-4935. https://doi.org/10.1016/j.matpr.2022.03.678
Danyal, M. M., Khan, S. S., Khan, M., Ullah, S., Mehmood, F., & Ali, I. (2024). Proposing sentiment analysis model based on BERT and XLNet for movie reviews. Multimedia Tools and Applications, 1-25. DOI: https://doi.org/10.1007/s11042-024-18156-5.
BBC.com. (2024). Dirty Vote: Film 'tentang kecurangan pilpres' tuai pro-kontra, bagaimana publik harus menyikapinya? [Artikel Berita]. URL: https://www.bbc.com/indonesia/articles/c72g1x45gj4o
Jurnalis Lingkungan Indonesia. (2024). Dirty Vote, Sebuah Desain Kecurangan Pemilu 2024 | Trailer [Video]. URL: https://www.youtube.com/watch?v=bXk9ZiRBtSY
Tirto. (2024). Link Nonton Film Dirty Vote Full Movie karya Dandhy Laksono [Artikel Berita]. URL: https://tirto.id/gVue
Kompas.com. (2024). Sutradara Ungkap Alasan Rilis Film "Dirty Vote" di Awal Masa Tenang Pemilu [Artikel Berita]. URL: https://nasional.kompas.com/read/2024/02/12/09463681/sutradara-ungkap-alasan-rilis-film-dirty-vote-di-awal-masa-tenang-pemilu
Kompas.TV. (2024). Fakta-fakta Film Dirty Vote, Rilis Jelang Pemilu hingga Biaya Patungan 20 Lembaga [Video]. URL: https://www.kompas.tv/entertainment/484601/fakta-fakta-film-dirty-vote-rilis-jelang-pemilu-hingga-biaya-patungan-20-lembaga
The Jakarta Post. (2024). Film Dirty Vote Causes Stir Days Before Election [Artikel Berita]. URL: https://www.thejakartapost.com/indonesia/2024/02/13/film-dirty-vote-causes-stir-days-before-election.html
Detik Jabar. (2024). Heboh Film Dirty Vote, Ini 7 Fakta di Baliknya! [Artikel Berita]. URL: https://www.detik.com/jabar/berita/d-7188446/heboh-film-dirty-vote-ini-7-fakta-di-baliknya
Ho, Q. T., Le, N. Q. K., & Ou, Y. Y. (2021). FAD-BERT: improved prediction of FAD binding sites using pre-training of deep bidirectional transformers. Computers in Biology and Medicine, 131, 104258. DOI: https://doi.org/10.1016/j.compbiomed.2021.104258
Duan, R., Huang, Z., Zhang, Y., Liu, X., & Dang, Y. (2021). Sentiment classification algorithm based on the cascade of bert model and adaptive sentiment dictionary. Wireless Communications and Mobile Computing, 2021, 1-8. DOI: https://doi.org/10.1155/2021/8785413
Mohammed, A. H., & Ali, A. H. (2021, July). Survey of bert (bidirectional encoder representation transformer) types. In Journal of Physics: Conference Series (Vol. 1963, No. 1, p. 012173). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1963/1/012173
Deepa, M. D. (2021). Bidirectional encoder representations from transformers (BERT) language model for sentiment analysis task. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(7), 1708-1721.
Mulyana, D. I., & Sahroni. (2024). Optimasi Penerapan Algoritma Yolo dan Data Augmentasi dalam Klasifikasi Pakaian Tradisional Kebaya. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 188–193. DOI: https://doi.org/10.35870/jtik.v8i1.1446
Agung, S. (2024). Implementasi Text Mining untuk Analisis Review pada Aplikasi Crowdfunding LX dan ST Menggunakan Metode Sentiment Analysis. LANCAH: Jurnal Inovasi Dan Tren, 2(1), 124~130. DOI: https://doi.org/10.35870/ljit.v2i1.2245
Pasaribu, N. A., & Sriani. (2023). The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 194–204. DOI: https://doi.org/10.35870/ijsecs.v3i3.1699
Gunawan, T. S., Ashraf, A., Riza, B. S., Haryanto, E. V., Rosnelly, R., Kartiwi, M., ... & Janin, Z. (2020). Development of video-based emotion recognition using deep learning with Google Colab. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(5), 2463. DOI: https://doi.org/10.12928/telkomnika.v18i5.16717
Mooers, B. H. M. (2021). Modernizing computing by structural biologists with Jupyter and Colab. Proceedings of the Python in Science Conference. DOI: https://doi.org/10.25080/majora-1b6fd038-002
Gupta, S., & Khade, N. (2020). Bert based multilingual machine comprehension in english and hindi. arXiv preprint arXiv:2006.01432. DOI: https://doi.org/10.48550/arXiv.2006.01432
Sutriawan, S., Andono, P. N., Muljono, M., & Pramunendar, R. A. (2023). Performance evaluation of classification algorithm for movie review sentiment analysis. International Journal of Computing, 7-14. DOI: https://doi.org/10.47839/ijc.22.1.2873
Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J., & Hsieh, J. (2021). Twitter sentiment analysis towards COVID-19 vaccines in the Philippines using naïve bayes. Information, 12(5), 204. DOI: https://doi.org/10.3390/info12050204
Chong, K., & Shah, N. (2022). Comparison of naive bayes and SVM classification in grid-search hyperparameter tuned and non-hyperparameter tuned healthcare stock market sentiment analysis. International Journal of Advanced Computer Science and Applications, 13(12).
Anreaja, L. J., Harefa, N. N., Negara, J. G. P., Pribyantara, V. N. H., & Prasetyo, A. B. (2022). Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia. JISA (Jurnal Informatika dan Sains), 5(1), 62-68. DOI: https://doi.org/10.31326/jisa.v5i1.1267.
Ardianto, R., Rivanie, T., Alkhalifi, Y., Nugraha, F. S., & Gata, W. (2020). Sentiment analysis on E-sports for education curriculum using naive Bayes and support vector machine. Jurnal Ilmu Komputer dan Informasi, 13(2), 109-122. DOI: https://doi.org/10.21609/jiki.v13i2.885
Cahyani, D. E., & Patasik, I. (2021). Performance comparison of tf-idf and word2vec models for emotion text classification. Bulletin of Electrical Engineering and Informatics, 10(5), 2780-2788. DOI: https://doi.org/10.11591/eei.v10i5.3157
Alaparthi, S., & Mishra, M. (2020). Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey. arXiv preprint arXiv:2007.01127. DOI: https://doi.org/10.48550/arXiv.2007.01127
Rao, A. C., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780. DOI: https://doi.org/10.1007/s10462-022-10144-1
Al-Saffar, A., & Omar, N. (2014). Study on feature selection and machine learning algorithms for Malay sentiment classification. Proceedings of the 6th International Conference on Information Technology and Multimedia. DOI: https://doi.org/10.1109/icimu.2014.7066643
Isnain, A. R., Marga, N. S., & Alita, D. (2021). Sentiment analysis of government policy on corona case using naive bayes algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(1), 55. DOI: https://doi.org/10.22146/ijccs.60718
Kaya, M., Fidan, G., & Toroslu, İ. H. (2012). Sentiment analysis of Turkish political news. 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. DOI: https://doi.org/10.1109/wi-iat.2012.115
Ressan, M. B., & Hassan, R. F. (2022). Naïve-bayes family for sentiment analysis during COVID-19 pandemic and classification tweets. Indonesian Journal of Electrical Engineering and Computer Science, 28(1), 375. DOI: https://doi.org/10.11591/ijeecs.v28.i1.pp375-383
Samih, A., Ghadi, A., & Fennan, A. (2023). Enhanced sentiment analysis based on improved word embeddings and XGBoost. International Journal of Electrical and Computer Engineering (IJECE), 13(2), 1827. DOI: https://doi.org/10.11591/ijece.v13i2.pp1827-1836.
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.
Lehečka, J., Švec, J., Ircing, P., & Šmídl, L. (2020, September). Bert-based sentiment analysis using distillation. In International Conference on Statistical Language and Speech Processing (pp. 58-70). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-59430-5_5
Selvakumar, B., & Lakshmanan, B. (2022). Sentimental analysis on user’s reviews using BERT. Materials Today: Proceedings, 62, 4931-4935. https://doi.org/10.1016/j.matpr.2022.03.678
Danyal, M. M., Khan, S. S., Khan, M., Ullah, S., Mehmood, F., & Ali, I. (2024). Proposing sentiment analysis model based on BERT and XLNet for movie reviews. Multimedia Tools and Applications, 1-25. DOI: https://doi.org/10.1007/s11042-024-18156-5.
BBC.com. (2024). Dirty Vote: Film 'tentang kecurangan pilpres' tuai pro-kontra, bagaimana publik harus menyikapinya? [Artikel Berita]. URL: https://www.bbc.com/indonesia/articles/c72g1x45gj4o
Jurnalis Lingkungan Indonesia. (2024). Dirty Vote, Sebuah Desain Kecurangan Pemilu 2024 | Trailer [Video]. URL: https://www.youtube.com/watch?v=bXk9ZiRBtSY
Tirto. (2024). Link Nonton Film Dirty Vote Full Movie karya Dandhy Laksono [Artikel Berita]. URL: https://tirto.id/gVue
Kompas.com. (2024). Sutradara Ungkap Alasan Rilis Film "Dirty Vote" di Awal Masa Tenang Pemilu [Artikel Berita]. URL: https://nasional.kompas.com/read/2024/02/12/09463681/sutradara-ungkap-alasan-rilis-film-dirty-vote-di-awal-masa-tenang-pemilu
Kompas.TV. (2024). Fakta-fakta Film Dirty Vote, Rilis Jelang Pemilu hingga Biaya Patungan 20 Lembaga [Video]. URL: https://www.kompas.tv/entertainment/484601/fakta-fakta-film-dirty-vote-rilis-jelang-pemilu-hingga-biaya-patungan-20-lembaga
The Jakarta Post. (2024). Film Dirty Vote Causes Stir Days Before Election [Artikel Berita]. URL: https://www.thejakartapost.com/indonesia/2024/02/13/film-dirty-vote-causes-stir-days-before-election.html
Detik Jabar. (2024). Heboh Film Dirty Vote, Ini 7 Fakta di Baliknya! [Artikel Berita]. URL: https://www.detik.com/jabar/berita/d-7188446/heboh-film-dirty-vote-ini-7-fakta-di-baliknya
Ho, Q. T., Le, N. Q. K., & Ou, Y. Y. (2021). FAD-BERT: improved prediction of FAD binding sites using pre-training of deep bidirectional transformers. Computers in Biology and Medicine, 131, 104258. DOI: https://doi.org/10.1016/j.compbiomed.2021.104258
Duan, R., Huang, Z., Zhang, Y., Liu, X., & Dang, Y. (2021). Sentiment classification algorithm based on the cascade of bert model and adaptive sentiment dictionary. Wireless Communications and Mobile Computing, 2021, 1-8. DOI: https://doi.org/10.1155/2021/8785413
Mohammed, A. H., & Ali, A. H. (2021, July). Survey of bert (bidirectional encoder representation transformer) types. In Journal of Physics: Conference Series (Vol. 1963, No. 1, p. 012173). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1963/1/012173
Deepa, M. D. (2021). Bidirectional encoder representations from transformers (BERT) language model for sentiment analysis task. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(7), 1708-1721.
Mulyana, D. I., & Sahroni. (2024). Optimasi Penerapan Algoritma Yolo dan Data Augmentasi dalam Klasifikasi Pakaian Tradisional Kebaya. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 8(1), 188–193. DOI: https://doi.org/10.35870/jtik.v8i1.1446
Agung, S. (2024). Implementasi Text Mining untuk Analisis Review pada Aplikasi Crowdfunding LX dan ST Menggunakan Metode Sentiment Analysis. LANCAH: Jurnal Inovasi Dan Tren, 2(1), 124~130. DOI: https://doi.org/10.35870/ljit.v2i1.2245
Pasaribu, N. A., & Sriani. (2023). The Shopee Application User Reviews Sentiment Analysis Employing Naïve Bayes Algorithm. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 194–204. DOI: https://doi.org/10.35870/ijsecs.v3i3.1699
Gunawan, T. S., Ashraf, A., Riza, B. S., Haryanto, E. V., Rosnelly, R., Kartiwi, M., ... & Janin, Z. (2020). Development of video-based emotion recognition using deep learning with Google Colab. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(5), 2463. DOI: https://doi.org/10.12928/telkomnika.v18i5.16717
Mooers, B. H. M. (2021). Modernizing computing by structural biologists with Jupyter and Colab. Proceedings of the Python in Science Conference. DOI: https://doi.org/10.25080/majora-1b6fd038-002
Gupta, S., & Khade, N. (2020). Bert based multilingual machine comprehension in english and hindi. arXiv preprint arXiv:2006.01432. DOI: https://doi.org/10.48550/arXiv.2006.01432
Sutriawan, S., Andono, P. N., Muljono, M., & Pramunendar, R. A. (2023). Performance evaluation of classification algorithm for movie review sentiment analysis. International Journal of Computing, 7-14. DOI: https://doi.org/10.47839/ijc.22.1.2873
Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J., & Hsieh, J. (2021). Twitter sentiment analysis towards COVID-19 vaccines in the Philippines using naïve bayes. Information, 12(5), 204. DOI: https://doi.org/10.3390/info12050204
Chong, K., & Shah, N. (2022). Comparison of naive bayes and SVM classification in grid-search hyperparameter tuned and non-hyperparameter tuned healthcare stock market sentiment analysis. International Journal of Advanced Computer Science and Applications, 13(12).
Anreaja, L. J., Harefa, N. N., Negara, J. G. P., Pribyantara, V. N. H., & Prasetyo, A. B. (2022). Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia. JISA (Jurnal Informatika dan Sains), 5(1), 62-68. DOI: https://doi.org/10.31326/jisa.v5i1.1267.
Ardianto, R., Rivanie, T., Alkhalifi, Y., Nugraha, F. S., & Gata, W. (2020). Sentiment analysis on E-sports for education curriculum using naive Bayes and support vector machine. Jurnal Ilmu Komputer dan Informasi, 13(2), 109-122. DOI: https://doi.org/10.21609/jiki.v13i2.885
Cahyani, D. E., & Patasik, I. (2021). Performance comparison of tf-idf and word2vec models for emotion text classification. Bulletin of Electrical Engineering and Informatics, 10(5), 2780-2788. DOI: https://doi.org/10.11591/eei.v10i5.3157
Alaparthi, S., & Mishra, M. (2020). Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey. arXiv preprint arXiv:2007.01127. DOI: https://doi.org/10.48550/arXiv.2007.01127
Rao, A. C., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780. DOI: https://doi.org/10.1007/s10462-022-10144-1
Al-Saffar, A., & Omar, N. (2014). Study on feature selection and machine learning algorithms for Malay sentiment classification. Proceedings of the 6th International Conference on Information Technology and Multimedia. DOI: https://doi.org/10.1109/icimu.2014.7066643
Isnain, A. R., Marga, N. S., & Alita, D. (2021). Sentiment analysis of government policy on corona case using naive bayes algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(1), 55. DOI: https://doi.org/10.22146/ijccs.60718
Kaya, M., Fidan, G., & Toroslu, İ. H. (2012). Sentiment analysis of Turkish political news. 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. DOI: https://doi.org/10.1109/wi-iat.2012.115
Ressan, M. B., & Hassan, R. F. (2022). Naïve-bayes family for sentiment analysis during COVID-19 pandemic and classification tweets. Indonesian Journal of Electrical Engineering and Computer Science, 28(1), 375. DOI: https://doi.org/10.11591/ijeecs.v28.i1.pp375-383
Samih, A., Ghadi, A., & Fennan, A. (2023). Enhanced sentiment analysis based on improved word embeddings and XGBoost. International Journal of Electrical and Computer Engineering (IJECE), 13(2), 1827. DOI: https://doi.org/10.11591/ijece.v13i2.pp1827-1836.