multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

TL;DR

multiMentalRoBERTa is a fine-tuned transformer model that accurately classifies multiple mental health conditions from social media text, offering interpretability and robustness for mental health support systems.

Contribution

The paper introduces multiMentalRoBERTa, a novel fine-tuned transformer model specifically designed for multiclass mental health disorder classification with superior performance and interpretability.

Findings

Achieves macro F1-scores of 0.839 (six-class) and 0.870 (five-class)

Outperforms MentalBERT and baseline classifiers in accuracy

Uses explainability methods to identify lexical cues for mental health detection

Abstract

The early detection of mental health disorders from social media text is critical for enabling timely support, risk assessment, and referral to appropriate resources. This work introduces multiMentalRoBERTa, a fine-tuned RoBERTa model designed for multiclass classification of common mental health conditions, including stress, anxiety, depression, post-traumatic stress disorder (PTSD), suicidal ideation, and neutral discourse. Drawing on multiple curated datasets, data exploration is conducted to analyze class overlaps, revealing strong correlations between depression and suicidal ideation as well as anxiety and PTSD, while stress emerges as a broad, overlapping category. Comparative experiments with traditional machine learning methods, domain-specific transformers, and prompting-based large language models demonstrate that multiMentalRoBERTa achieves superior performance, with macro F1-scores of 0.839 in the six-class setup and 0.870 in the five-class setup (excluding stress), outperforming both fine-tuned MentalBERT and baseline classifiers. Beyond predictive accuracy, explainability methods, including Layer Integrated Gradients and KeyBERT, are applied to identify lexical cues that drive classification, with a particular focus on distinguishing depression from suicidal ideation. The findings emphasize the effectiveness of fine-tuned transformers for reliable and interpretable detection in sensitive contexts, while also underscoring the importance of fairness, bias mitigation, and human-in-the-loop safety protocols. Overall, multiMentalRoBERTa is presented as a lightweight, robust, and deployable solution for enhancing support in mental health platforms.