Ensemble transformer with post-hoc explanations for depression emotion and severity detection

This study presents an ensemble transformer framework for detecting depression-related emotions and classifying their severity in social media text. It addresses the need for scalable and trustworthy AI solutions in mental health by integrating four transformer models. The DepTformer-XAI-SV model uses a weighted soft-voting mechanism based on validation macro-F1 scores to improve accuracy and incorporates LIME to highlight key linguistic features associated with depression. The framework is evaluated on two benchmark datasets: DepressionEmo, with eight emotion classes, and the merged depression severity detection (MDSD), with four severity levels, both sourced from social media. To address class imbalance, we…