Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives

• URL: http://arxiv.org/abs/2507.09565v2
• Date: Thu, 17 Jul 2025 06:11:39 GMT
• Title: Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives
• Authors: Heba Shakeel, Tanvir Ahmad, Chandni Saxena,
• Abstract summary: We introduce a dataset for classifying wellness dimensions in social media user posts, covering six key aspects: physical, emotional, social, intellectual, spiritual, and vocational.<n>The dataset is designed to capture these dimensions in user-generated content, with a comprehensive annotation framework developed under the guidance of domain experts.<n>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.
• Score: 1.0446041735532203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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