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This is a Quantitative Study study (also classified as: Experimental Study).

September 12, 2025Open Access

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

MKM. R. Pavan KumarVellore Institute of Technology University LKLal KhanGachon University ACAhyoung ChoiGachon University

Key Points

RAMHA achieves 92% accuracy in binary emotion classification on the GoEmotions dataset, and shows high performance across multiple metrics.
In extensive experiments, RAMHA consistently outperformed eight baseline models, including BERT and LSTM, highlighting its superiority in precision and recall.
The model integrates RoBERTa and adapter layers, optimized with focal loss to tackle class imbalance in emotion classification tasks.
The findings suggest that RAMHA could enhance emotion-aware mental health screening in both clinical and social environments.

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.

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