What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You
The recently released ChatGPT, an artificial intelligence conversational agent, has garnered significant attention in academia and real life. A multitude of early ChatGPT users have eagerly explored its capabilities and shared their opinions on social media, providing valuable feedback. Both user qu...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/183 |
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| author | Rui Wang Xing Liu Peng Ren Shuyu Chang Zhengxin Huang Haiping Huang Guozi Sun |
| author_facet | Rui Wang Xing Liu Peng Ren Shuyu Chang Zhengxin Huang Haiping Huang Guozi Sun |
| author_sort | Rui Wang |
| collection | DOAJ |
| description | The recently released ChatGPT, an artificial intelligence conversational agent, has garnered significant attention in academia and real life. A multitude of early ChatGPT users have eagerly explored its capabilities and shared their opinions on social media, providing valuable feedback. Both user queries and social media posts have been instrumental in expressing public concerns regarding this advanced dialogue system. To comprehensively understand these public concerns, a novel Self-Supervised Neural Topic Model (SSTM), which formulates topic modeling as a representation learning procedure, is proposed in this paper. The proposed SSTM utilizes Dirichlet prior matching and three regularization terms for improved modeling performance. Extensive experiments on three publicly available text corpora (Twitter Posts, Subreddit and queries from ChatGPT users) demonstrate the effectiveness of the proposed approach in extracting higher-quality public concerns. Moreover, the SSTM performs competitively across all three datasets regarding topic diversity and coherence metrics. Based on the extracted topics, we could gain valuable insights into the public’s concerns regarding technologies like ChatGPT, enabling us to formulate effective strategies to address these issues. |
| format | Article |
| id | doaj-art-eb2aad75317c4ed29e066e8b86292209 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-eb2aad75317c4ed29e066e8b862922092025-01-24T13:39:39ZengMDPI AGMathematics2227-73902025-01-0113218310.3390/math13020183What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells YouRui Wang0Xing Liu1Peng Ren2Shuyu Chang3Zhengxin Huang4Haiping Huang5Guozi Sun6School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaDepartment of Computer Science, Youjiang Medical University for Nationalities, Baise 533000, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaThe recently released ChatGPT, an artificial intelligence conversational agent, has garnered significant attention in academia and real life. A multitude of early ChatGPT users have eagerly explored its capabilities and shared their opinions on social media, providing valuable feedback. Both user queries and social media posts have been instrumental in expressing public concerns regarding this advanced dialogue system. To comprehensively understand these public concerns, a novel Self-Supervised Neural Topic Model (SSTM), which formulates topic modeling as a representation learning procedure, is proposed in this paper. The proposed SSTM utilizes Dirichlet prior matching and three regularization terms for improved modeling performance. Extensive experiments on three publicly available text corpora (Twitter Posts, Subreddit and queries from ChatGPT users) demonstrate the effectiveness of the proposed approach in extracting higher-quality public concerns. Moreover, the SSTM performs competitively across all three datasets regarding topic diversity and coherence metrics. Based on the extracted topics, we could gain valuable insights into the public’s concerns regarding technologies like ChatGPT, enabling us to formulate effective strategies to address these issues.https://www.mdpi.com/2227-7390/13/2/183text mininginformation extractionneural topic modelChatGPTsocial media analysis |
| spellingShingle | Rui Wang Xing Liu Peng Ren Shuyu Chang Zhengxin Huang Haiping Huang Guozi Sun What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You Mathematics text mining information extraction neural topic model ChatGPT social media analysis |
| title | What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You |
| title_full | What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You |
| title_fullStr | What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You |
| title_full_unstemmed | What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You |
| title_short | What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You |
| title_sort | what are the public s concerns about chatgpt a novel self supervised neural topic model tells you |
| topic | text mining information extraction neural topic model ChatGPT social media analysis |
| url | https://www.mdpi.com/2227-7390/13/2/183 |
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