EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916563984056320 |
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| author | Pyreddy, Shireesh Reddy Zaman, Tarannum Shaila |
| author_facet | Pyreddy, Shireesh Reddy Zaman, Tarannum Shaila |
| contents | The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further understand the emotional intelligence of ChatGPT, we examine its responses to selected tweets, highlighting differences in sentiment between human comments and LLM-generated responses. We introduce EmoXpt, a sentiment analysis framework designed to assess both human perspectives on generative AI and the sentiment embedded in ChatGPT's responses. Unlike prior studies that focus exclusively on human sentiment, EmoXpt uniquely evaluates the emotional expression of ChatGPT. Experimental results demonstrate that LLM-generated responses are notably more efficient, cohesive, and consistently positive than human responses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses Pyreddy, Shireesh Reddy Zaman, Tarannum Shaila Machine Learning Computation and Language Human-Computer Interaction The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further understand the emotional intelligence of ChatGPT, we examine its responses to selected tweets, highlighting differences in sentiment between human comments and LLM-generated responses. We introduce EmoXpt, a sentiment analysis framework designed to assess both human perspectives on generative AI and the sentiment embedded in ChatGPT's responses. Unlike prior studies that focus exclusively on human sentiment, EmoXpt uniquely evaluates the emotional expression of ChatGPT. Experimental results demonstrate that LLM-generated responses are notably more efficient, cohesive, and consistently positive than human responses. |
| title | EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses |
| topic | Machine Learning Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.06597 |