Mapping Public Sentiment on Generative AI via Twitter NLP and Topic Modeling*
DOI:
https://doi.org/10.70062/globalscience.v1i4.183Keywords:
Generative Artificial Intelligence; Sentiment Analysis; BERTopic Modeling; Public Perception; Social Media AnalyticsAbstract
Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *
References
Appiah, E., & Htait, A. (2024). AI in the Public Eye: Analysing Social Media Sentiment and Opinion on Artificial Intelligence. Proceedings of Machine Learning Research, 295. https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014764960&partnerID=40&md5=d9b033a34a6b0e61dde375159bd551cd
Are, C., Briggs, P., & Brown, R. (2025). Content creators’ hopes and fears about artificial intelligence. Convergence, 31(6), 1901–1925. https://doi.org/10.1177/13548565251372830
Awadallah, M. S., De Arriba-Perez, F., Costa-Montenegro, E., Kholief, M., & El-Bendary, N. (2022). Investigation of Local Interpretable Model-Agnostic Explanations (LIME) Framework with Multi-Dialect Arabic Text Sentiment Classification. 32nd International Conference on Computer Theory and Applications, ICCTA 2022 - Proceedings, 116–121. https://doi.org/10.1109/ICCTA58027.2022.10206274
Chen, W., Hussain, W., & Chen, J. (2025). GLMTopic: A Hybrid Chinese Topic Model Leveraging Large Language Models. Computers, Materials and Continua, 85(1), 1559–1583. https://doi.org/10.32604/cmc.2025.065916
Cohen, M., Khavkin, M., Movsowitz Davidow, D., & Toch, E. (2024). ChatGPT in the public eye: Ethical principles and generative concerns in social media discussions. New Media and Society. https://doi.org/10.1177/14614448241279034
Fang, Z., Alqazlan, L., Liu, D., He, Y., & Procter, R. (2023). A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, 505–522. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159856552&partnerID=40&md5=755cee5c2807c4103318278300ca170c
Gao, S., Norkute, M., & Agrawal, A. (2024). Evaluating Interactive Topic Models in Applied Settings. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3613905.3637133
Harshvardhan, G. M., Gourisaria, M. K., Sahu, A., Rautaray, S. S., & Pandey, M. (2021). Topic modelling twitterati sentiments using latent dirichlet allocation during demonetization. Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development, INDIACom 2021, 811–815. https://doi.org/10.1109/INDIACom51348.2021.00145
Huang, A., Zhang, F., & Song, C. (2025). Multimodal Sentiment Analysis of Online Product Marketing Information Based on Artificial Intelligence Neural Networks and Text Mining. IEEE Transactions on Engineering Management, 72, 3182–3199. https://doi.org/10.1109/TEM.2025.3589199
Jeon, J., Kim, L., & Park, J. (2025). The ethics of generative AI in social science research: A qualitative approach for institutionally grounded AI research ethics. Technology in Society, 81, 102836. https://doi.org/https://doi.org/10.1016/j.techsoc.2025.102836
Jeong, H., & Sung, M. (2025). AI in the Public Eye: Decoding Perception of Generative AI Through Natural Language Processing. Asian Communication Research, 22(1), 27–48. https://doi.org/10.20879/acr.2025.22.006
Jin, Y., Choi, M., Verma, G., Wang, J., & Kumar, S. (2024). MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 6192–6210. https://doi.org/10.18653/v1/2024.findings-acl.370
Jonnala, S., Thomas, N. M., & Mishra, S. (2025). Navigating ethical minefields: a multi-stakeholder approach to assessing interconnected risks in generative AI using grey DEMATEL. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1611024
Kim, J. H., Jung, H. S., Park, M. H., Lee, S. H., Lee, H., Kim, Y., & Nan, D. (2022). Exploring Cultural Differences of Public Perception of Artificial Intelligence via Big Data Approach. Communications in Computer and Information Science, 1580 CCIS, 427–432. https://doi.org/10.1007/978-3-031-06417-3_57
Lampropoulos, G., Ferdig, R., & Kaplan-Rakowski, R. (2025). a social media data analysis of general and educational use of ChatGPT: Understanding emotional educators through Twitter data. Educational Technology and Society, 28(3), 51–65. https://doi.org/10.30191/ETS.202507_28(3).SP05
Li, Y., Mandaloju, T., & Chen, H. (2025). Exploring Public Perceptions of Generative AI in Libraries: A Social Media Analysis of X Discussions. Proceedings of the Association for Information Science and Technology, 62(1), 406–416. https://doi.org/10.1002/pra2.1266
Liao, J., & Lee, C. S. (2025). Public Perceptions of Generative AI: Insights from Social Q&A Platforms. Communications in Computer and Information Science, 2529 CCIS, 349–358. https://doi.org/10.1007/978-3-031-94171-9_33
Liu, C., Tian, Y., Shi, Y., Huang, Z., & Shao, Y. (2024). An analysis of public topics and sentiments based on social media during the COVID-19 Omicron Variant outbreak in Shanghai 2022. Computational Urban Science, 4(1). https://doi.org/10.1007/s43762-024-00128-y
Liu, P., Rigoulot, S., & Pell, M. D. (2015). Cultural differences in on-line sensitivity to emotional voices: Comparing East and West. Frontiers in Human Neuroscience, 9(May). https://doi.org/10.3389/fnhum.2015.00311
Medhat, M., Ayoub, L. W., Daher, M., & Mohamed, K. M. (2025). Ethical Considerations in AI-Generated Content on Social Media. In Studies in Big Data (Vol. 171, pp. 611–620). https://doi.org/10.1007/978-3-031-83911-5_52
Mehra, V., Sood, S., & Singh, P. (2025). Understanding social media mood during global events: a sentiment and topic modeling study of FIFA 2022 Tweets. Engineering Research Express, 7(4). https://doi.org/10.1088/2631-8695/ae1d0d
Miyazaki, K., Murayama, T., Uchiba, T., An, J., & Kwak, H. (2024). Public perception of generative AI on Twitter: an empirical study based on occupation and usage. EPJ Data Science, 13(1), 2. https://doi.org/10.1140/epjds/s13688-023-00445-y
Møgelvang, A., Bjelland, C., Grassini, S., & Ludvigsen, K. (2024). Gender Differences in the Use of Generative Artificial Intelligence Chatbots in Higher Education: Characteristics and Consequences. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121363
Ou, Y., Zhang, P., Yu, J., Li, M., Su, S., Zhang, M., Feng, R., Sun, F., & Wu, J. (2025). The Application of the BERTopic Model in Natural Language Processing: In-Depth Text Topic Modeling. 2025 5th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2025, 793–796. https://doi.org/10.1109/ICCECE65250.2025.10984639
Panjaitan, C. H. P., Manongga, D., & Mayopu, R. G. (2025). A Systematic Literature Review on Multimodal Sentiment Analysis Trends, Challenges and Opportunities. Proceeding - 2025 4th International Conference on Creative Communication and Innovative Technology: Empowering Transformative MATURE LEADERSHIP: Harnessing Technological Advancement for Global Sustainability, ICCIT 2025. https://doi.org/10.1109/ICCIT65724.2025.11167526
Pota, M., Ventura, M., Fujita, H., & Esposito, M. (2021). Multilingual evaluation of pre-processing for BERT-based sentiment analysis of tweets. Expert Systems with Applications, 181, 115119. https://doi.org/https://doi.org/10.1016/j.eswa.2021.115119
Radanliev, P. (2025). AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development. Applied Artificial Intelligence, 39(1). https://doi.org/10.1080/08839514.2025.2463722
Sbei, A., Elbedoui, K., & Barhoumi, W. (2025). Assessing the Efficiency of Transformer Models with Varying Sizes for Text Classification: A Study of Rule-Based Annotation with DistilBERT and Other Transformers. Vietnam Journal of Computer Science, 12(3), 301–328. https://doi.org/10.1142/S2196888824500209
Shao, W. (2025). Text sentiment classification optimization based on a fine-tuned BERT and large language model. Journal of Computational Methods in Sciences and Engineering. https://doi.org/10.1177/14727978251355795
Shehu, H. A., Haidar Sharif, M., Uyaver, S., Tokat, S., & Ramadan, R. A. (2020). Sentiment analysis of turkish twitter data using polarity lexicon and artificial intelligence. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 332 LNICST, 113–125. https://doi.org/10.1007/978-3-030-60036-5_8
Siddhanta, A., & Bhagat, A. K. (2025). Sentiment Showdown - Sentence Transformers stand their ground against Language Models: Case of Sentiment Classification using Sentence Embeddings. Procedia Computer Science, 257, 1205–1212. https://doi.org/10.1016/j.procs.2025.03.161
Smith, A., Kumar, V., Boyd-Graber, J., Seppi, K., & Findlater, L. (2018). Closing the loop: User-centered design and evaluation of a human-in-the-loop topic modeling system. International Conference on Intelligent User Interfaces, Proceedings IUI, 293–304. https://doi.org/10.1145/3172944.3172965
Sun, L., & Zhou, L. (2025). Generative artificial intelligence attitude analysis of undergraduate students and their precise improvement strategies: A differential analysis of multifactorial influences. Education and Information Technologies, 30(8), 10591–10626. https://doi.org/10.1007/s10639-024-13236-3
Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9). https://doi.org/10.1093/pnasnexus/pgae346
Tijare, P. V, & Jhansi, R. P. (2024). Analyzing Twitter Sentiment Trends during the FIFA World Cup 2022. 2024 1st International Conference for Women in Computing, InCoWoCo 2024 - Proceedings. https://doi.org/10.1109/InCoWoCo64194.2024.10863620
Xiao, H., & Luo, L. (2024). An Automatic Sentiment Analysis Method for Short Texts Based on Transformer-BERT Hybrid Model. IEEE Access, 12, 93305–93317. https://doi.org/10.1109/ACCESS.2024.3422268
Zhao, Y., Li, Z., Zhang, J., Yu, X., Tong, Y., & Tsai, S. (2025). Design of an Enterprise Public Opinion Monitoring System Based on Natural Language Processing: Sentiment Analysis and Management of Public Opinion Data. Journal of Global Information Management, 33(1). https://doi.org/10.4018/JGIM.381306
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