心理健康领域的人工智能：大型语言模型对抑郁、焦虑和压力查询回应的情感与情绪分析
AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
AI总结：
本文研究了八个大型语言模型对不同用户画像下抑郁、焦虑和压力查询的情绪回应，发现模型选择和心理健康状况类型显著影响情绪表达，而人口统计学因素影响甚微，为心理健康应用中的模型选择提供了依据。
摘要：
抑郁、焦虑和压力是普遍存在的心理健康问题，越来越多的人向大型语言模型（LLMs）寻求信息。本研究调查了八个LLM（Claude Sonnet、Copilot、Gemini Pro、GPT-4o、GPT-4o mini、Llama、Mixtral和Perplexity）如何回答二十个关于抑郁、焦虑和压力的实用问题，这些问题针对六种用户画像（基线、女性、男性、年轻人、老年人和大学生）进行设定。这些模型生成了2,880个回答，我们使用最先进的工具对其情感和情绪进行了评分。我们的分析表明，乐观、恐惧和悲伤在所有输出中主导了情绪景观，中性情感始终保持较高数值。感激、喜悦和信任以中等水平出现，而愤怒、厌恶和爱等情绪很少被表达。LLM的选择显著影响了情绪表达模式。Mixtral表现出最高水平的负面情绪，包括不赞成、烦恼和悲伤，而Llama则展现出最乐观和喜悦的回应。心理健康状况的类型极大地塑造了情绪回应：焦虑提示引发了极高的恐惧分数（0.974），抑郁提示产生了升高的悲伤（0.686）和最高的负面情感，而与压力相关的查询则产生了最乐观的回应（0.755），并伴有升高的喜悦和信任。相比之下，查询的人口统计学设定仅在情绪基调上产生了微小的变化。统计分析证实了显著的模型特定和状况特定差异，而人口统计学影响仍然很小。这些发现强调了在心理健康应用中选择模型的关键重要性，因为每个LLM都表现出独特的情绪特征，这可能显著影响用户体验和结果。
英文摘要：
Depression, anxiety, and stress are widespread mental health concerns that increasingly drive individuals to seek information from Large Language Models (LLMs). This study investigates how eight LLMs (Claude Sonnet, Copilot, Gemini Pro, GPT-4o, GPT-4o mini, Llama, Mixtral, and Perplexity) reply to twenty pragmatic questions about depression, anxiety, and stress when those questions are framed for six user profiles (baseline, woman, man, young, old, and university student). The models generated 2,880 answers, which we scored for sentiment and emotions using state-of-the-art tools. Our analysis revealed that optimism, fear, and sadness dominated the emotional landscape across all outputs, with neutral sentiment maintaining consistently high values. Gratitude, joy, and trust appeared at moderate levels, while emotions such as anger, disgust, and love were rarely expressed. The choice of LLM significantly influenced emotional expression patterns. Mixtral exhibited the highest levels of negative emotions including disapproval, annoyance, and sadness, while Llama demonstrated the most optimistic and joyful responses. The type of mental health condition dramatically shaped emotional responses: anxiety prompts elicited extraordinarily high fear scores (0.974), depression prompts generated elevated sadness (0.686) and the highest negative sentiment, while stress-related queries produced the most optimistic responses (0.755) with elevated joy and trust. In contrast, demographic framing of queries produced only marginal variations in emotional tone. Statistical analyses confirmed significant model-specific and condition-specific differences, while demographic influences remained minimal. These findings highlight the critical importance of model selection in mental health applications, as each LLM exhibits a distinct emotional signature that could significantly impact user experience and outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
