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Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations (2306.12951v2)
Published 22 Jun 2023 in cs.CL
Abstract: ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a LLM. While it demonstrates state-of-the-art capabilities in a variety of language-generation tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we investigated public attitudes towards ChatGPT by applying natural language processing techniques such as sentiment analysis and topic modeling to Twitter data from December 5, 2022 to June 10, 2023. Our sentiment analysis result indicates that the overall sentiment was largely neutral to positive, and negative sentiments were decreasing over time. Our topic model reveals that the most popular topics discussed were Education, Bard, Search Engines, OpenAI, Marketing, and Cybersecurity, but the ranking varies by month. We also analyzed the occupations of Twitter users and found that those with occupations in arts and entertainment tweeted aboutChatGPT most frequently. Additionally, people tended to tweet about topics relevant to their occupation. For instance, Cybersecurity is the most discussed topic among those with occupations related to computer and math, and Education is the most discussed topic among those in academic and research. Overall, our exploratory study provides insights into the public perception of ChatGPT, which could be valuable to both the general public and developers of this technology.
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(2022) Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334 (2022) https://doi.org/10.48550/ARXIV.2205.14334 Zhou et al. (2023) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. 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(2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Mirowski, P., Mathewson, K.W., Pittman, J., Evans, R.: Co-writing screenplays and theatre scripts with language models: An evaluation by industry professionals. arXiv preprint arXiv:2209.14958 (2022) Lee et al. (2022) Lee, M., Liang, P., Yang, Q.: Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–19 (2022). https://doi.org/10.1145/3491102.3502030 Borji (2023) Borji, A.: A categorical archive of chatgpt failures. arXiv preprint arXiv:2302.03494 (2023) Lin et al. (2022) Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334 (2022) https://doi.org/10.48550/ARXIV.2205.14334 Zhou et al. (2023) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lee, M., Liang, P., Yang, Q.: Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–19 (2022). https://doi.org/10.1145/3491102.3502030 Borji (2023) Borji, A.: A categorical archive of chatgpt failures. arXiv preprint arXiv:2302.03494 (2023) Lin et al. (2022) Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334 (2022) https://doi.org/10.48550/ARXIV.2205.14334 Zhou et al. (2023) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Borji, A.: A categorical archive of chatgpt failures. arXiv preprint arXiv:2302.03494 (2023) Lin et al. (2022) Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334 (2022) https://doi.org/10.48550/ARXIV.2205.14334 Zhou et al. (2023) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334 (2022) https://doi.org/10.48550/ARXIV.2205.14334 Zhou et al. (2023) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. 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Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. 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Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. 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(2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334 (2022) https://doi.org/10.48550/ARXIV.2205.14334 Zhou et al. (2023) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhou, K., Jurafsky, D., Hashimoto, T.: Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439 (2023) https://doi.org/10.48550/ARXIV.2302.13439 Nature Machine Intelligence (2023) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. 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(2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. 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Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Nature Machine Intelligence: The ai writing on the wall. Nature Machine Intelligence 5(1), 1–1 (2023) https://doi.org/10.1038/s42256-023-00613-9 Han et al. (2022) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Benson, L., Sun, L., Zubova, E., Qiao, Y., Burtell, M., et al.: Folio: Natural language reasoning with first-order logic. arXiv preprint arXiv:2209.00840 (2022) https://doi.org/10.48550/ARXIV.2209.00840 Jakesch et al. (2023) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., Naaman, M.: Co-writing with opinionated language models affects users’ views. arXiv preprint arXiv:2302.00560 (2023) Lin et al. (2021) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Lin, S., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 (2021) https://doi.org/10.48550/ARXIV.2109.07958 Weidinger et al. (2021) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021) https://doi.org/10.48550/ARXIV.2112.04359 Zhuo et al. (2023) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. 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(2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z.: Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023) https://doi.org/10.48550/ARXIV.2301.12867 Abid et al. (2021) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abid, A., Farooqi, M., Zou, J.: Large language models associate muslims with violence. Nature Machine Intelligence 3(6), 461–463 (2021) Buchanan et al. (2021) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Buchanan, B., Lohn, A., Musser, M.: Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, ??? (2021) O’Connor and ChatGPT (2022) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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- O’Connor, S., ChatGPT: Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Education in Practice 66, 103537–103537 (2022) https://doi.org/10.1016/j.nepr.2022.103537 Transformer and Zhavoronkov (2022) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. 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(2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Transformer, C.G.P.-t., Zhavoronkov, A.: Rapamycin in the context of pascal’s wager: generative pre-trained transformer perspective. Oncoscience 9, 82 (2022) https://doi.org/10.18632/oncoscience.571 Kung et al. (2022) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. 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- Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V.: Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. medRxiv (2022) https://doi.org/10.1101/2022.12.19.22283643 https://www.medrxiv.org/content/early/2022/12/21/2022.12.19.22283643.full.pdf Anon (2023) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Anon: Tools such as chatgpt threaten transparent science; here are our ground rules for their use. Nature 613, 612 (2023) Thorp (2023) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. 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(2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Thorp, H.H.: ChatGPT is fun, but not an author. American Association for the Advancement of Science (2023) Frewer et al. (1998) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Frewer, L.J., Howard, C., Shepherd, R.: Understanding public attitudes to technology. Journal of Risk Research 1(3), 221–235 (1998) de Cosmo et al. (2021) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cosmo, L.M., Piper, L., Di Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing 2021, 83–102 (2021) Bii et al. (2018) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Universal Journal of Educational Research 6(7), 1586–1597 (2018) Abdullah et al. (2022) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. 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(2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Abdullah, M., Madain, A., Jararweh, Y.: Chatgpt: Fundamentals, applications and social impacts. In: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8 (2022). IEEE Dwivedi et al. (2023) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. 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Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. 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(2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M., et al.: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management 71, 102642 (2023) Biswas (2023) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Biswas, S.S.: Role of chat gpt in public health. Annals of Biomedical Engineering, 1–2 (2023) Tlili et al. (2023) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., Agyemang, B.: What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments 10(1), 15 (2023) Dempere et al. (2023) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Dempere, J., Modugu, K.P., Hesham, A., Ramasamy, L.: The impact of chatgpt on higher education. Dempere J, Modugu K, Hesham A and Ramasamy LK (2023) The impact of ChatGPT on higher education. Front. Educ 8, 1206936 (2023) Shoufan (2023) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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(2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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(2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Shoufan, A.: Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access (2023) Tounsi et al. (2023) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tounsi, A., Elkefi, S., Bhar, S.L.: Exploring the reactions of early users of chatgpt to the tool using twitter data: Sentiment and topic analyses. In: 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 1–6 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10150870 Korkmaz et al. (2023) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Korkmaz, A., Aktürk, C., TALAN, T.: Analyzing the user’s sentiments of chatgpt using twitter data. Iraqi Journal For Computer Science and Mathematics 4(2), 202–214 (2023) Haque et al. (2022) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ” i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data. arXiv preprint arXiv:2212.05856 (2022) Taecharungroj (2023) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Taecharungroj, V.: “what can chatgpt do?” analyzing early reactions to the innovative ai chatbot on twitter. Big Data and Cognitive Computing 7(1), 35 (2023) Leiter et al. (2023) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., Eger, S.: Chatgpt: A meta-analysis after 2.5 months. arXiv preprint arXiv:2302.13795 (2023) Blei et al. (2003) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003) Okey et al. (2023) Okey, O.D., Udo, E.U., Rosa, R.L., Rodríguez, D.Z., Kleinschmidt, J.H.: Investigating chatgpt and cybersecurity: A perspective on topic modeling and sentiment analysis. Computers & Security 135, 103476 (2023) Praveen and Vajrobol (2023) Praveen, S., Vajrobol, V.: Understanding the perceptions of healthcare researchers regarding chatgpt: a study based on bidirectional encoder representation from transformers (bert) sentiment analysis and topic modeling. Annals of Biomedical Engineering, 1–3 (2023) Li et al. (2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. 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Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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(2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. 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Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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(2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. 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No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. 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(2023) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., Hemphill, L.: Chatgpt in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv:2305.02201 (2023) Fütterer et al. (2023) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., Gerjets, P.: Chatgpt in education: Global reactions to ai innovations (2023) Hutto and Gilbert (2014) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. 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(2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. 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(2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. 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Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014). https://doi.org/10.1609/icwsm.v8i1.14550 Barbieri et al. (2020) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Barbieri, F., Camacho-Collados, J., Neves, L., Espinosa-Anke, L.: Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421 (2020) Loureiro et al. (2022) Loureiro, D., Barbieri, F., Neves, L., Anke, L.E., Camacho-Collados, J.: Timelms: Diachronic language models from twitter. arXiv preprint arXiv:2202.03829 (2022) Broder (1997) Broder, A.Z.: On the resemblance and containment of documents. In: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pp. 21–29 (1997). IEEE Barbieri et al. (2022) Barbieri, F., Anke, L.E., Camacho-Collados, J.: Xlm-t: Multilingual language models in twitter for sentiment analysis and beyond. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 258–266 (2022). https://doi.org/10.48550/arXiv.2104.12250 Grootendorst (2022) Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Cheng and Jiang (2022) Cheng, Y., Jiang, H.: Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product & Brand Management 31(2), 252–264 (2022) Frith (2023) Frith, K.H.: Chatgpt: Disruptive educational technology. Nursing Education Perspectives 44(3), 198–199 (2023) Chow et al. (2023) Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. 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- Chow, J.C., Sanders, L., Li, K.: Impact of chatgpt on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence 6 (2023) Choi et al. (2023) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019)
- Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023) Tubishat et al. (2023) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Tubishat, M., Al-Obeidat, F., Shuhaiber, A.: Sentiment analysis of using chatgpt in education. In: 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–7 (2023). IEEE. https://ieeexplore.ieee.org/abstract/document/10215977 Rosenthal et al. (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019) Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019)
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- Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 (2019)
- Ratanond Koonchanok (4 papers)
- Yanling Pan (2 papers)
- Hyeju Jang (7 papers)