对比人类与ChatGPT对生成式AI的情感表达，发现模型回应更积极且连贯。
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
- 构建情感分析框架EmoXpt，同时评估人类与ChatGPT的情感表达
- 模型回复比人类评论更高效、连贯，且情绪更一致积极
- 适合关注AI情感智能与人机交互的研究者阅读
生成式AI的广泛应用引发多元观点，人们对其应用既有支持也有批评。本研究通过分析提及ChatGPT、OpenAI、Copilot和LLMs等关键词的人类推文，探究生成式AI相关的情感动态。为理解ChatGPT的情感智能，我们考察其对精选推文的回应，揭示人类评论与模型生成回复间的情感差异。提出EmoXpt情感分析框架，用于评估人类对生成式AI的观点及ChatGPT回复中蕴含的情绪。与以往仅关注人类情感的研究不同，EmoXpt独特地评估了ChatGPT的情绪表达。实验表明，大语言模型生成的回复在效率、连贯性及情绪一致性上显著优于人类评论。
原文摘要 · Abstract (English)
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.
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