MH-EABERT: an adaptive fusion approach with emotion knowledge-enhanced BERT for explainable mental health sentiment analysis from social media

While deep learning models achieve high accuracy in mental health screening, their inherent “black box” nature precludes clinical trust, and pure rule-based systems fail to dynamically grasp complex linguistic nuances such as sarcasm. To rigorously bridge this critical research gap, this study introduces MH-EABERT (Mental Health Emotion-Augmented BERT), a novel neuro-symbolic framework that seamlessly integrates domain-specific clinical knowledge with deep semantic learning to heavily enhance both classification accuracy and explicit explainability. We construct a Mental Health Emotion Lexicon (MHEL) that maps 13,872 emotion terms and 181 clinical phrases to six cognitive dimensions.

The architecture operates across five distinct computational modules, utilizing an Emotion Knowledge Extraction (EKE) scorer to apply 10 clinical logic rules that distinguish pathological states from general distress. To prevent semantic distortion, a Self-Adaptive Fusion (SAF) algorithm dynamically governs knowledge injection based on continuous confidence calibration. Using mathematical “hard positioning,” we embed clinical context directly into the sentence structure, forcing BERT’s attention mechanism to process it as integral semantics.

Comprehensive evaluation utilizing 5-fold cross-validation repeated across 5 random seeds on three diverse datasets—Reddit Communities (151,288 posts), Comprehensive MH (53,043 statements), and Synthetic Posts (1000 samples)—demonstrated statistically significant superiority (\(p < 0.0001\)) over state-of-the-art baselines including RoBERTa, XLNet, DistilBERT, MentalBERT, and ClinicalBERT. Crucially, all evaluated models were rigorously trained and tested under the exact same experimental setup. MH-EABERT achieved a peak accuracy of 87.97% on Reddit data and crucially reduced the False Negative Rate for suicidal detection from 14.67 to 8.55%.

Extensive ablation studies and SHAP analyses confirmed that predictions are grounded in transparent, auditable clinical features. MH-EABERT represents a significant advancement toward clinically grounded, highly interpretable AI systems capable of reliable, automated triage from unstructured social media text. To ensure absolute reproducibility, the code, lexicon, and framework weights are publicly available at https://doi.org/10.5281/zenodo.23285908.

The authors of the manuscript acknowledge ELITE Research Lab for providing the computational resources and support necessary for this research. Furthermore, during preparation of our manuscript, Generative AI tools were used to improve language and readability of the manuscript. The authors reviewed the contents as needed after using the tools and take full responsibility for the contents of the manuscripts.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, 1229, Bangladesh ELITE Research Lab LLC, 17010 Cedarcroft Rd, Queens, NY, USA Sayedur Rahman, Touhid Alam & Md Kishor Morol Faculty of Information Science and Technology, Multimedia University, 75450, Melaka, Malaysia Centre for Intelligent Cloud Computing (CICC), COE of Advanced Cloud, Faculty of Information Science & Technology, Multimedia University, 75450, Melaka, Malaysia The authors declare no competing interests. This study used publicly available, anonymized social media text data and did not involve direct interaction or intervention with human participants.