RAMHA: A Hybrid Social Text-Based Transformer with Adapter for Mental Health Emotion Classification

Mahander Kumar, Lal Khan () and Ahyoung Choi ()
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Mahander Kumar: Department of Computer Science, Mir Chakar Khan Rind University, Sibi 82000, Pakistan
Lal Khan: Department of AI and SW, Gachon University, Seongnam 13120, Republic of Korea
Ahyoung Choi: Department of AI and SW, Gachon University, Seongnam 13120, Republic of Korea

Mathematics, 2025, vol. 13, issue 18, 1-26

Abstract: Depression, stress, and anxiety are mental health disorders that are increasingly becoming a huge challenge in the digital age; at the same time, it is critical that they are detected early. Social media is a rich and complex source of emotional expressions that requires intelligent systems that can decode subtle psychological states from natural language. This paper presents RAMHA (RoBERTa with Adapter-based Mental Health Analyzer), a hybrid deep learning model that combines RoBERTa, parameter-efficient adapter layers, BiLSTM, and attention mechanisms and is further optimized with focal loss to address the class imbalance problem. When tested on three filtered versions of the GoEmotions dataset, RAMHA shows outstanding results, with a maximum accuracy of 92% in binary classification and 88% in multiclass tasks. A large number of experiments are performed to compare RAMHA with eight standard baseline models, including SVM, LSTM, and BERT. In these experiments, RAMHA is able to consistently outperform the other models in terms of accuracy, precision, recall, and F1-score. Ablation studies further confirm the contributions of the individual components of the architecture, and comparative analysis demonstrates that RAMHA outperforms the best previously reported F1-scores by a substantial margin. The results of our study not only indicate the potential of the adapter-enhanced transformer in emotion-aware mental health screening but also establish a solid basis for its use in clinical and social settings.

Keywords: mental health emotion; deep learning; transformers; classification; Bi-LSTM; attention; roBERTa (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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