Analisis Sentimen Publik Terhadap ChatGPT Menggunakan Arsitektur Transformer: A Systematic Literature Review
Keywords:
Sentiment analysis, ChatGPT, BERT, transformer
Abstract
Sentiment analysis of public opinion regarding ChatGPT has become an increasingly popular research topic in the fields of Natural Language Processing (NLP) and deep learning since its public release in November 2022. This systematic literature review aims to provide a comprehensive reference on the approaches, methods, and techniques employed in analyzing public sentiment toward ChatGPT using transformer-based models. The review examined studies published between 2022 and 2026 and retrieved from the Scopus database. After a rigorous selection process, 30 articles met the inclusion criteria and were analyzed.
The findings reveal that no eligible publications were identified in 2022, while a significant increase occurred in 2024, accounting for 53.3% of the total studies. BERT and its variants, including ALBERT and the BERT+RoBERTa ensemble, were the most widely adopted approaches, appearing in 40.0% of the reviewed articles, with standard and fine-tuned BERT models representing 30.0%. The reported average F1-score was 0.904, ranging from 0.87 to 0.93. Twitter/X was the most frequently used data source (53.3%), and English was the dominant language in 86.7% of the studies. Despite the strong performance achieved by transformer-based models, challenges remain in handling sarcasm, informal language, and multilingual contexts. This review provides an up-to-date overview of current research trends and highlights future opportunities to enhance sentiment analysis systems for generative artificial intelligence technologies.
References
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Mujahid, M., Kanwal, K., Rustam, F., Aljedaani, W., & Ashraf, I. (2023). Arabic ChatGPT Tweets Classification Using RoBERTa and BERT Ensemble Model. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(8), 1–23. https://doi.org/10.1145/3605889
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Safira, W., Prabaswara, B., Stevens Karnyoto, A., & Pardamean, B. (2025). Leveraging ALBERT for Sentiment Classification of Long-Form ChatGPT Reviews on Twitter. International Journal of Computing and Digital Systems, 17(1), 1–12. https://doi.org/10.12785/ijcds/1570999256
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Stryker, C., & Bergmann, D. (n.d.). What is a transformer model? Retrieved June 8, 2026, from https://www.ibm.com/think/topics/transformer-model
Su, Y., & Kabala, Z. J. (2023). Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica. Data, 8(12), 180. https://doi.org/10.3390/data8120180
Sudheesh, R., Mujahid, M., Rustam, F., Shafique, R., Chunduri, V., Villar, M. G., … Ashraf, I. (2023). Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach. Information (Switzerland), 14(9). https://doi.org/10.3390/info14090474
Triawan, P., Tahyudin, I., & Purwadi, P. (2025). Impact of NLP Algorithms on Sentiment Analysis Efficiency and Accuracy. Journal of Information Systems and Informatics, 7(3), 2684–2709. https://doi.org/10.51519/journalisi.v7i3.1222
Xing, Y., Zhang, J. Z., Teng, G., & Zhou, X. (2024). Voices in the digital storm: Unraveling online polarization with ChatGPT. Technology in Society, 77, 102534. https://doi.org/10.1016/j.techsoc.2024.102534
Xu, Z., Fang, Q., Huang, Y., & Xie, M. (2024a). The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling. PLoS ONE, 19(5 May). https://doi.org/10.1371/journal.pone.0302502
Xu, Z., Fang, Q., Huang, Y., & Xie, M. (2024b). The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling. PLOS ONE, 19(5), e0302502. https://doi.org/10.1371/journal.pone.0302502
Zhang, F., Guo, R., Xing, W., Zhu, W., Kim, T., & Liu, Z. (2024). WIP: From Tweets to Trends: Tracing the Public’s Perception of AI in Education Post-ChatGPT. 2024 IEEE Frontiers in Education Conference (FIE), 1–5. IEEE. https://doi.org/10.1109/FIE61694.2024.10893041
Alobedalla, A., Hatamleh, A., Alawneh, M., Alsawaftah, H., & Obeidat, R. (2025). ChatGPT and Public Emotion: A Study of Arabic Opinions on Twitter. 2025 16th International Conference on Information and Communication Systems (ICICS), 1–6. IEEE. https://doi.org/10.1109/ICICS65354.2025.11072958
Amrullah, A. (2025). Sentiment Analysis in the Age of Transformers and Large Language Models: A Comprehensive Review of Recent Advances and Future (Vol. 1).
Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370. https://doi.org/10.1016/j.techsoc.2023.102370
Danish, M., Amjad, M., Dyussekeyev, K., & Zhumadillayeva, A. (2025). Exploring ChatGPT’s Role in Education: An NLP-Based Sentiment and Thematic Study Using Twitter Data. Procedia Computer Science, 259, 959–968. https://doi.org/10.1016/j.procs.2025.04.049
Darma Pamuja, S., Studi Teknologi Informasi, P., Kesehatan dan Teknologi, F., & Muhammadiyah Klaten, U. (2025). Optimasi Analisis Sentimen Ulasan Platform Pendidikan Daring Menggunakan Arsitektur ALBERT dan Teknik Augmentasi Kontekstual (Vol. 01).
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Retrieved from http://arxiv.org/abs/1810.04805
Dewangga, B. ., & Prasetiyo, R. T. (2025). Comparative Analysis of BERT Model and DistilBERT Model for Enhanced Clickbait Headline Structure Detection in Indonesian Online News (Vol. 13).
El Azzouzy, O., Chanyour, T., & Andaloussi, S. J. (2025). Transformer-based models for sentiment analysis of YouTube video comments. Scientific African, 29. https://doi.org/10.1016/j.sciaf.2025.e02836
Elmadya, D., & Oktavia, T. (2025). Comprehensive Sentiment Analysis of ChatGPT App Reviews Using Lexicon-Based Methods, Deep Learning, and Machine Learning. 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), 786–791. IEEE. https://doi.org/10.1109/ICoCSETI63724.2025.11019303
Fahmi, M. I., & Nababan, A. A. (2025). A Comparative Study of BERT and RoBERTa for Sentiment Analysis on Twitter Data Related to Mental Health. Mathematical Modelling of Engineering Problems, 12(9), 3289–3295. https://doi.org/10.18280/mmep.120931
Fajri, F., Tutuko, B., & Sukemi, S. (2022). Membandingkan Nilai Akurasi BERT dan DistilBERT pada Dataset Twitter. 8(2), 71–80.
Hannani, M., Soudi, A., & Van Laerhoven, K. (2024). Assessing the Performance of ChatGPT-4, Fine-tuned BERT and Traditional ML Models on Moroccan Arabic Sentiment Analysis. Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, 489–498. Stroudsburg, PA, USA: Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.nlp4dh-1.47
Issa, E., & Abdu Ibrahim, A. (2024). Integrating BERT for Nuanced Sentiment Analysis: A Detailed Examination of Diverse Textual Datasets. IEEE Access, 12, 186296–186312. https://doi.org/10.1109/ACCESS.2024.3510944
Koonchanok, R., Pan, Y., & Jang, H. (2024). Public attitudes toward chatgpt on twitter: sentiments, topics, and occupations. Social Network Analysis and Mining, 14(1), 106. https://doi.org/10.1007/s13278-024-01260-7
Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., & Eger, S. (2024). ChatGPT: A meta-analysis after 2.5 months. Machine Learning with Applications, 16, 100541. https://doi.org/10.1016/j.mlwa.2024.100541
Lyu, Z., Lin, J., & Cheng, W. (2025). Public Trust in ChatGPT: A BERT-Based Framework for Analyzing Social Media Discourse. International Journal of Human–Computer Interaction, 1–20. https://doi.org/10.1080/10447318.2025.2545454
Mathebula, M., Modupe, A., & Marivate, V. (2024). ChatGPT as a Text Annotation Tool to Evaluate Sentiment Analysis on South African Financial Institutions. IEEE Access, 12, 144017–144043. https://doi.org/10.1109/ACCESS.2024.3464374
Muhammad, I., & Rospocher, M. (2025). On Assessing the Performance of LLMs for Target-Level Sentiment Analysis in Financial News Headlines. Algorithms, 18(1), 46. https://doi.org/10.3390/a18010046
Mujahid, M., Kanwal, K., Rustam, F., Aljedaani, W., & Ashraf, I. (2023). Arabic ChatGPT Tweets Classification Using RoBERTa and BERT Ensemble Model. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(8), 1–23. https://doi.org/10.1145/3605889
Naing, S. Z. S., & Udomwong, P. (2024a). Public Opinions on ChatGPT: An Analysis of Reddit Discussions by Using Sentiment Analysis, Topic Modeling, and SWOT Analysis. Data Intelligence, 6(2), 344–374. https://doi.org/10.1162/dint_a_00250
Naing, S. Z. S., & Udomwong, P. (2024b). Public Opinions on ChatGPT : An Analysis of Reddit Discussions by Using Sentiment Analysis, Topic Modeling, and SWOT Analysis. Data Intelligence, 6(2), 344–374. https://doi.org/10.1162/dint_a_00250
Padarha, S., & Vijaylakshmi, S. (2024). A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/10.4108/eetiot.6447
Safira, W., Prabaswara, B., Stevens Karnyoto, A., & Pardamean, B. (2025). Leveraging ALBERT for Sentiment Classification of Long-Form ChatGPT Reviews on Twitter. International Journal of Computing and Digital Systems, 17(1), 1–12. https://doi.org/10.12785/ijcds/1570999256
Sinaga, F. M., Pangaribuan, J. J., -, K., -, F., & Widjaja, A. E. (2025). Dynamic Sentiment Analysis on the Emergence of Pre-Trained Generative Model-Based Applications in Indonesia. International Journal of Advanced Computer Science and Applications, 16(12). https://doi.org/10.14569/IJACSA.2025.01612111
Stryker, C., & Bergmann, D. (n.d.). What is a transformer model? Retrieved June 8, 2026, from https://www.ibm.com/think/topics/transformer-model
Su, Y., & Kabala, Z. J. (2023). Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica. Data, 8(12), 180. https://doi.org/10.3390/data8120180
Sudheesh, R., Mujahid, M., Rustam, F., Shafique, R., Chunduri, V., Villar, M. G., … Ashraf, I. (2023). Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach. Information (Switzerland), 14(9). https://doi.org/10.3390/info14090474
Triawan, P., Tahyudin, I., & Purwadi, P. (2025). Impact of NLP Algorithms on Sentiment Analysis Efficiency and Accuracy. Journal of Information Systems and Informatics, 7(3), 2684–2709. https://doi.org/10.51519/journalisi.v7i3.1222
Xing, Y., Zhang, J. Z., Teng, G., & Zhou, X. (2024). Voices in the digital storm: Unraveling online polarization with ChatGPT. Technology in Society, 77, 102534. https://doi.org/10.1016/j.techsoc.2024.102534
Xu, Z., Fang, Q., Huang, Y., & Xie, M. (2024a). The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling. PLoS ONE, 19(5 May). https://doi.org/10.1371/journal.pone.0302502
Xu, Z., Fang, Q., Huang, Y., & Xie, M. (2024b). The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling. PLOS ONE, 19(5), e0302502. https://doi.org/10.1371/journal.pone.0302502
Zhang, F., Guo, R., Xing, W., Zhu, W., Kim, T., & Liu, Z. (2024). WIP: From Tweets to Trends: Tracing the Public’s Perception of AI in Education Post-ChatGPT. 2024 IEEE Frontiers in Education Conference (FIE), 1–5. IEEE. https://doi.org/10.1109/FIE61694.2024.10893041
Published
2026-07-01
How to Cite
Afifah, A., Sudarmilah, E., & Nugroho, Y. S. (2026). Analisis Sentimen Publik Terhadap ChatGPT Menggunakan Arsitektur Transformer: A Systematic Literature Review. JIIFKOM (Jurnal Ilmiah Informatika Dan Komputer), 5(2), 25-38. https://doi.org/https://doi.org/10.51901/jiifkom.v5i02.729
Section
Articles
