Citation

Hossain, Moinul and Khaleque, Taskin and Islam, Md Jahidul and Das, Sourav Rabi and Noman, Abdullah Al and Morol, Md Kishor and Liew, Tze Hui and Nandi, Dip (2026) ProStack-MH: An Explainable Stacked Transformer Approach to Anxiety–Depression Differentiation in Real-World Mental-Health Discussions. IEEE Access, 14. pp. 112865-112890. ISSN 2169-3536

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Abstract

Mental-health issues are difficult to identify in their early stages within online platforms because of the complexity, inconsistency, and narrative character of user-generated text. The development of natural-language processing (NLP) and transformer architectures opens new possibilities for largescale automated detection of psychological distress signals. In this context, the present work constructs a computational pipeline that classifies real-world mental-health forum posts as indicating anxiety or depression—two clinically distinct yet linguistically overlapping conditions. The proposed end-to-end system, ProStack-MH (Probabilistic Stacking for Mental-Health Text Classification), combines rigorous dataset cleaning, noise elimination, staff-content removal, semantic text normalization, transformer-friendly preprocessing, class-balancing, and user-level data partitioning to prevent leakage. ProStack-MH stacks two complementary transformer encoders—roberta-base and distilroberta-base—whose softmax probability vectors are concatenated and passed to a lightweight Logistic Regression meta-learner. Six baselines are evaluated under a strict leakage-free, user-wise test split: classical TF-IDF + LR/SVM, BiLSTM, DistilBERT, MentalBERT, DeBERTa, and RoBERTa-base. Model behaviour is explained post-hoc using SHAP feature attributions and GoEmotionsbased affective profiling. An ablation study confirms that the stacking step—rather than either base learner alone—drives the improvement. ProStack-MH achieves 93.38% accuracy, F1 = 0.9302, MCC = 0.8674, and ROC-AUC = 0.98, outperforming all baselines on every metric. This framework provides a reproducible methodological foundation for computational mental-health monitoring, risk screening, and earlyintervention research.

Item Type:	Article
Uncontrolled Keywords:	Computational mental health
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 03:33
Last Modified:	04 Aug 2026 03:33
URII:	http://shdl.mmu.edu.my/id/eprint/16470

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