A Computational Analysis of Emotions and Topics in YouTube Discourse on Sora
Source abstract
Abstract
As generative artificial intelligence (AI) technologies become increasingly present in creative and professional domains, examining public discourse surrounding these tools is important for understanding their broader social implications. This study conducts a two-part analysis of the initial public reaction to Sora, the generative video model developed by OpenAI, by analyzing 23,543 English-language comments posted on YouTube between February and April 2024. Rather than relying on traditional positive–negative sentiment classifications, this study integrates fine-grained emotion detection with topic modeling to examine the relationship between emotions and topics in the discourse. Based on the residual analysis, the overall association between topics and emotions was weak; however, certain topics were associated with specific emotions. For instance, ethical discussions were more likely to be associated with sadness and anger, artistic settings were associated with fear, and benchmark discussions were associated with joy. Methodologically, this study utilizes an emotion–topic coupling through residual deviation with a hierarchical LDA-BERTopic approach, bringing together computational modeling and theories of emotion. This study provides a structured and theory-based way to explore the affective and thematic patterns in the public’s discourse surrounding Sora.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Computational and Text Analysis Methods · Mental Health via Writing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Yildiz Technical University, İstanbul 34220, Türkiye
References (76)
Show source-provided references
1. Dalgali, A., and Crowston, K. (2026, February 26). Algorithmic Journalism and Its Impacts on Work. Computation + Journalism Symposium. Available online: https://cj2020.northeastern.edu/.
2. Dalgali, A., and Crowston, K. (2020, January 7–10). Factors Influencing Approval of Wikipedia Bots. Proceedings of the Hawaii International Conference on System Sciences, Maui, HI, USA. Available online: http://hdl.handle.net/10125/63757.10.24251/HICSS.2020.018 ↗
3. Framing and feelings on social media: The futures of work and intelligent machines10.1108/ITP-01-2023-0049 ↗
4. Wang, K., Lin, L., Abdallah, M., and Cai, W. (2025). Where is the Boundary? Understanding How People Recognize and Evaluate Generative AI-extended Videos. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 26 April–1 May 2025, ACM.10.1145/3706598.3714061 ↗
5. Exploring the Early Adoption of Open AI among Laypeople and Technical Professionals: An Analysis of Twitter Conversations on #ChatGPT and #GPT310.1080/10447318.2023.2295725 ↗
6. (2026, February 22). OpenAI. Sora|OpenAI. Available online: https://openai.com/sora/.
7. OpenAI (2026, February 22). Sora System Card|OpenAI. Available online: https://openai.com/index/sora-system-card/.
8. Unveiling sora open AI’s impact: A review of transformative shifts in marketing and advertising employment10.1080/23311975.2024.2440640 ↗
9. Adetayo, A.J., Enamudu, A.I., Lawal, F.M., and Odunewu, A.O. (2024). From text to video with AI: The rise and potential of Sora in education and libraries. Libr. Hi Tech. News.10.1108/LHTN-02-2024-0028 ↗
10. Chen, Z., Li, S., and Haque, M.A. (2024). An Overview of OpenAI’s Sora and Its Potential for Physics Engine Free Games and Virtual Reality. EAI Endorsed Trans. AI Robot., 3.10.4108/airo.5273 ↗
11. OpenAI’s Sora in ophthalmology: Revolutionary generative AI in eye health10.1038/s41433-024-03098-x ↗
12. Liu, Y., Zhang, K., Li, Y., Yan, Z., Gao, C., Chen, R., Yuan, Z., Huang, Y., Sun, H., and Gao, J. (2024). Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models. arXiv.
13. Concerns with OpenAI’s Sora in Medicine10.1007/s10439-024-03505-0 ↗
14. Ocal, A. (2023). Perceptions of AI Ethics on Social Media. Proceedings of the 2023 IEEE International Symposium on Ethics in Engineering, Science and Technology (ETHICS), West Lafayette, IN, USA, 8–20 May 2023, IEEE.10.1109/ETHICS57328.2023.10155069 ↗
15. Sora for Computational Social Systems: From Counterfactual Experiments to Artificiofactual Experiments with Parallel Intelligence10.1109/TCSS.2024.3373928 ↗
16. Achanta, A. (2025). Synthetic Lies: Security and Ethical Challenges of Deceptive Content in Generative AI. Proceedings of the 2025 3rd World Conference on Communication & Computing (WCONF), Raipur, India, 25–27 July 2025, IEEE.10.1109/WCONF64849.2025.11233471 ↗
17. Unmasking deepfakes: A multidisciplinary examination of social impacts and regulatory responses10.1007/s42454-025-00060-4 ↗
18. Beyond A Reasonable Doubt? Audiovisual Evidence, AI Manipulation, Deepfakes, and the Law10.1109/TTS.2024.3427816 ↗
19. Gamage, D., Ghasiya, P., Bonagiri, V., Whiting, M.E., and Sasahara, K. (2022). Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications. Proceedings of the CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April–5 May 2022, ACM.10.1145/3491102.3517446 ↗
20. Generative AI and deepfakes: A human rights approach to tackling harmful content10.1080/13600869.2024.2324540 ↗
21. Wiederhold, B.K. (2026). The End of Visual Certainty: How AI-Generated Video Reshapes Trust and Perception. Cyberpsychology Behav. Soc. Netw., 21522715261417287.10.1177/21522715261417287 ↗
22. Chesney, R., and Citron, D.K. (2018). Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. SSRN J., 107.10.2139/ssrn.3213954 ↗
23. Covington, P., Adams, J., and Sargin, E. (2016). Deep Neural Networks for YouTube Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15–19 September 2016, ACM.10.1145/2959100.2959190 ↗
24. Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube10.1145/3392854 ↗
25. Ribeiro, M.H., Ottoni, R., West, R., Almeida, V.A.F., and Meira, W. (2020). Auditing radicalization pathways on YouTube. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, 27–30 January 2020, ACM.10.1145/3351095.3372879 ↗
26. Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don’t Talk to Conservatives10.1609/icwsm.v15i1.18105 ↗
27. Hristova, G., and Netov, N. (2022, January 17–20). Media Coverage and Public Perception of Distance Learning During the COVID-19 Pandemic: A Topic Modeling Approach Based on BERTopic. Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan.10.1109/BigData55660.2022.10020466 ↗
28. Ocal, A. (2023). Framing, Emotions, Salience: The Future of AI as Seen by Redditors. [Ph.D. Thesis, Syracuse University]. Available online: https://www.proquest.com/docview/2845416849.
29. Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach10.1109/ACCESS.2024.3510526 ↗
30. Reasoning in social media: Insights from Reddit ‘Change My View’ submissions10.1108/OIR-08-2020-0330 ↗
31. A Cross-Platform Comparison of Public Discussions Regarding ChatGPT on Twitter and Weibo: Data-Driven Analysis10.1080/10447318.2025.2508311 ↗
32. Analyzing the students’ views, concerns, and perceived ethics about chat GPT usage10.1016/j.caeai.2023.100180 ↗
33. Viewpoint: When Will AI Exceed Human Performance? Evidence from AI Experts10.1613/jair.1.11222 ↗
34. Just another clickbait title: A corpus-driven investigation of negative attitudes toward science on Reddit10.1177/09636625221146453 ↗
35. Using Data from Reddit, Public Deliberation, and Surveys to Measure Public Opinion about Autonomous Vehicles10.1093/poq/nfab021 ↗
36. BERT-Based Sentiment Analysis of Turkish e-Commerce Reviews: Star Ratings Versus Text
37. Advancing emotion recognition in social media: A novel integration of heterogeneous neural networks with fine-tuned language models10.1016/j.ipm.2024.103974 ↗
38. Ocal, A. (2025). Cinematic Narratives as Socio-Technical Systems: Emotion Mining and Script–Audience Emotional Fidelity. Systems, 13.10.3390/systems13110994 ↗
39. A survey on deep learning for textual emotion analysis in social networks10.1016/j.dcan.2021.10.003 ↗
40. Knowledge-based BERT word embedding fine-tuning for emotion recognition10.1016/j.neucom.2023.126488 ↗
41. Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., and Ravi, S. (2020). GoEmotions: A Dataset of Fine-Grained Emotions. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5–10 July 2020, Association for Computational Linguistics.10.18653/v1/2020.acl-main.372 ↗
42. Li, Y., Su, H., Shen, X., Li, W., Cao, Z., and Niu, S. (2017). DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Taipei, Taiwan, 27 November–1 December 2017, Asian Federation of Natural Language Processing. Available online: https://aclanthology.org/I17-1099.
43. Rosenthal, S., Farra, N., and Nakov, P. (2017). SemEval-2017 Task 4: Sentiment Analysis in Twitter. Proceedings of the 11th International Workshop on Semantic Evaluation, Vancouver, Canada, 3–4 August 2017, Association for Computational Linguistics.10.18653/v1/S17-2088 ↗
44. The Future of Work in the Age of Automation: Proceedings of a Workshop on Norbert Wiener’s 21st Century Legacy10.1109/TTS.2024.3476041 ↗
45. An argument for basic emotions10.1080/02699939208411068 ↗
46. Parrott, W.G. (2001). Emotions in Social Psychology: Essential Readings, Psychology Press.
47. Abbasi, M.M., and Beltiukov, A. (2020). Analyzing the Complex Nature of Emotions from Written Text Using Parrott Theory of Emotions. Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020), Ufa, Stavropol, Khanty-Mansiysk, Russia, 10 November 2020, Atlantis Press.10.2991/aisr.k.201029.020 ↗
48. (2026, February 28). dair-ai/emotion · Datasets at Hugging Face. Available online: https://huggingface.co/datasets/dair-ai/emotion.
49. Saravia, E., Liu, H.-C.T., Huang, Y.-H., Wu, J., and Chen, Y.-S. (2018). CARER: Contextualized Affect Representations for Emotion Recognition. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October–1 November 2018, Association for Computational Linguistics.10.18653/v1/D18-1404 ↗
50. Fast, E., and Horvitz, E. (2017, January 4–9). Long-Term Trends in the Public Perception of Artificial Intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, USA.10.1609/aaai.v31i1.10635 ↗
51. Plutchik, R. (2000). Emotions in the Practice of Psychotherapy: Clinical Implications of Affect Theories, American Psychological Association.10.1037/10366-000 ↗
52. Public perception of generative AI on Twitter: An empirical study based on occupation and usage10.1140/epjds/s13688-023-00445-y ↗
53. Bostrom, N., and Yudkowsky, E. (2011). The Ethics of Artificial Intelligence. Cambridge Handbook of Artificial Intelligence, Cambridge University Press.
54. AI ethics should not remain toothless! A call to bring back the teeth of ethics10.1177/2053951720942541 ↗
55. Ocal, A. (2025). Moral Decision-making with Artificial Intelligence. Proceedings of the 2025 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), Evanston, IL, USA, 6–8 June 2025, IEEE.10.1109/ETHICS65148.2025.11098393 ↗
56. Cho, J., Puspitasari, F.D., Zheng, S., Zheng, J., Lee, L.H., Kim, T.H., Hong, C.S., and Zhang, C. (2024). Sora as an AGI World Model? A Complete Survey on Text-to-Video Generation. arXiv.
57. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv.
58. Analyzing Reddit Discourse Surrounding Generative AI
59. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text10.1609/icwsm.v8i1.14550 ↗
60. Future Progress in Artificial Intelligence: A Survey of Expert Opinion10.1007/978-3-319-26485-1_33 ↗
61. Expert and Non-expert Opinion About Technological Unemployment10.1007/s11633-018-1127-x ↗
62. Qi, W., Pan, J., Lyu, H., and Luo, J. (2023). Excitements and Concerns in the Post-ChatGPT Era: Deciphering Public Perception of AI through Social Media Analysis. arXiv.10.1016/j.tele.2024.102158 ↗
63. Latent Dirichlet Allocation
64. Smart literature review: A practical topic modelling approach to exploratory literature review10.1186/s40537-019-0255-7 ↗
65. ktrain: A Low-Code Library for Augmented Machine Learning
66. Method agreement analysis: A review of correct methodology10.1016/j.theriogenology.2010.01.003 ↗
67. The Measurement of Observer Agreement for Categorical Data10.2307/2529310 ↗
68. Banjade, R., Niraula, N.B., Maharjan, N., Rus, V., Stefanescu, D., Lintean, M., and Gautam, D. (2015). NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, 10 May 2015, Association for Computational Linguistics.10.18653/v1/S15-2030 ↗
69. Cohen, J. (2009). Statistical Power Analysis for the Behavioral Sciences, Psychology Press. [2nd ed.]. Reprint.
70. A classification scheme for content analyses of YouTube video comments10.1108/JD-06-2012-0078 ↗
71. Öcal, A. (2015). Belirtisiz Mantıktan Yararlanılarak Ortaöğretim Matematik Öğretmenliği Öğrencilerinin Öğretmenlik Uygulaması Başarılarının Değerlendirilmesi. [Master’s Thesis, Hacettepe University].
72. Pew Research Center (2025, March 14). From Chat GPT to DALL-E, How Americans See New Uses of AI. Available online: https://www.pewresearch.org/short-reads/2023/02/22/how-americans-view-emerging-uses-of-artificial-intelligence-including-programs-to-generate-text-or-art/?utm_source=chatgpt.com.
73. Mogavi, R.H., Wang, D., Tu, J., Hadan, H., Sgandurra, S.A., Hui, P., and Nacke, L.E. (2024). Sora OpenAI’s Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generation. arXiv.
74. Brynjolfsson, E., McAfee, A., and Cummings, J. (2014). The Second Machine Age Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company.
75. Dalgali, A., and Crowston, K. (2019, January 8–11). Sharing Open Deep Learning Models. Proceedings of the 52nd Hawaii International Conference on System Sciences, Maui, HI, USA.10.24251/HICSS.2019.256 ↗
76. Otonom araçlara yönelik duygusal tutumlar: BERT modeli ile duygu analizi10.17341/gazimmfd.1652024 ↗