构建首个六维健康分析数据集，助力社交媒体心理状态智能评估
Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives
- 设计六维健康标注框架，实现用户文本中身心健康维度的精准分类
- 跨10折交叉验证，模型平均F1得分达0.78，具备良好分类性能
- 适用于心理健康监测、个性化干预系统研发，兼具可解释性与伦理合规
我们提出一个用于分类社交媒体用户帖子中健康维度的数据集，涵盖身体、情绪、社交、智力、精神和职业六个核心方面。该数据集基于领域专家指导的全面标注框架构建，可对文本片段进行准确归类。我们评估了传统机器学习模型与先进Transformer模型在多类别分类任务上的表现，采用精确率、召回率与F1分数进行评估，结果为10折交叉验证的平均值。通过事后解释方法确保模型决策的透明性与可解释性。该数据集支持区域化健康评估，推动个性化幸福感分析及心理健康早期干预策略的发展。研究严格遵守伦理规范，实验与数据集已公开发布于Github。
原文摘要 · Abstract (English)
We introduce a dataset for classifying wellness dimensions in social media user posts, covering six key aspects: physical, emotional, social, intellectual, spiritual, and vocational. The dataset is designed to capture these dimensions in user-generated content, with a comprehensive annotation framework developed under the guidance of domain experts. This framework allows for the classification of text spans into the appropriate wellness categories. We evaluate both traditional machine learning models and advanced transformer-based models for this multi-class classification task, with performance assessed using precision, recall, and F1-score, averaged over 10-fold cross-validation. Post-hoc explanations are applied to ensure the transparency and interpretability of model decisions. The proposed dataset contributes to region-specific wellness assessments in social media and paves the way for personalized well-being evaluations and early intervention strategies in mental health. We adhere to ethical considerations for constructing and releasing our experiments and dataset publicly on Github.
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