Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives
This work addresses mental health monitoring through social media analysis, but it is incremental as it focuses on dataset creation and standard model evaluation.
The paper tackles the problem of classifying wellness dimensions in social media posts by introducing a dataset covering six aspects, and evaluates machine learning models with performance metrics like F1-score from cross-validation.
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.