用NLP与大模型分析社交媒体文本，提升心理健康评估能力
psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis
- 结合LSTM、BERT和大模型进行心理状态分析
- 摘要任务中取得顶尖一致性与矛盾性得分
- 代码开源，助力心理健康研究与应用
社交媒体帖子是通过自动化分析工具检测心理健康状态和用户福祉的丰富数据源。本文展示了我们在CLPsych 2026共享任务中，运用多种自然语言处理方法（包括LSTM、基于BERT的模型及大语言模型）进行自我状态与福祉分析及摘要生成。我们的方法在摘要任务中取得了顶级的一致性与矛盾性评分，其他任务表现处于中等水平。通过测试与开发此类心理健康状态估计系统，我们为改善心理健康支持体系作出了贡献。相关代码已公开：https://github.com/psytechlab/CLPsych2026/。
原文摘要 · Abstract (English)
Social media posts are a rich and valuable source of data for analyzing mental health states and users' well-being using automated analysis tools. In this work, we demonstrate how we used a range of Natural Language Processing (NLP) methods, including Long Short-Term Memory (LSTM), BERT-based models, and Large Language Models (LLMs), for self-state and well-being analysis and summarization during the CLPsych Shared Task 2026. Our approach achieved one of the top Consistency and Contradiction scores for the summarization task and also middle-level results for the other tasks. By testing and developing such mental health-state estimation systems, we contributed to improving mental health support systems. We make our code available https://github.com/psytechlab/CLPsych2026/.
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