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

TL;DR

Holistix introduces a comprehensive dataset for classifying six wellness dimensions in social media posts, enabling better mental health analysis and personalized interventions through machine learning models.

Contribution

This paper presents a new annotated dataset and evaluation framework for classifying wellness dimensions in social media content, supporting mental health research.

Findings

Transformer models outperform traditional methods in classification accuracy.

Post-hoc explanations improve model interpretability.

The dataset enables region-specific wellness assessments.

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.