用大模型分析大学生心理健康支持反馈，提升服务评估效率
Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models
- 构建人机协作的语义数据集，用于分析学生心理支持反馈
- GPT-3.5与BERT在情感识别上表现最佳，准确率超基准方法
- 为高校心理服务优化提供数据驱动决策支持，适合教育管理者
高校心理健康支持对学生成长至关重要，涵盖心理咨询与支持性活动。但其效果评估面临数据收集难、缺乏标准化指标等问题，研究受限。学生反馈是关键，却多依赖定性分析，未系统应用先进机器学习方法。本文利用公开的Student Voice Survey数据，采用大语言模型（LLMs）分析学生对心理健康支持的态度。我们通过人机协作构建了名为SMILE-College的情感分析数据集。对比传统机器学习与前沿LLM方法，发现GPT-3.5和BERT在该数据集上表现最优。分析揭示了准确预测回应情感的挑战，并提出大模型可有效增强心理健康相关研究，改进高校心理服务。该数据驱动方法有助于实现高效、科学的心理健康支持评估与管理决策。
原文摘要 · Abstract (English)
Mental health support in colleges is vital in educating students by offering counseling services and organizing supportive events. However, evaluating its effectiveness faces challenges like data collection difficulties and lack of standardized metrics, limiting research scope. Student feedback is crucial for evaluation but often relies on qualitative analysis without systematic investigation using advanced machine learning methods. This paper uses public Student Voice Survey data to analyze student sentiments on mental health support with large language models (LLMs). We created a sentiment analysis dataset, SMILE-College, with human-machine collaboration. The investigation of both traditional machine learning methods and state-of-the-art LLMs showed the best performance of GPT-3.5 and BERT on this new dataset. The analysis highlights challenges in accurately predicting response sentiments and offers practical insights on how LLMs can enhance mental health-related research and improve college mental health services. This data-driven approach will facilitate efficient and informed mental health support evaluation, management, and decision-making.
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