Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media

• URL: http://arxiv.org/abs/2507.19511v1
• Date: Thu, 17 Jul 2025 04:58:31 GMT
• Title: Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
• Authors: Khalid Hasan, Jamil Saquer, Mukulika Ghosh,
• Abstract summary: This study provides a comprehensive evaluation of state-of-the-art transformer models against Long Short-Term Memory (LSTM) based approaches.<n>We construct a large annotated dataset using different text embedding techniques for mental health disorder classification on Reddit.<n> Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches.
• Score: 0.16385815610837165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against Long Short-Term Memory (LSTM) based approaches using different text embedding techniques for mental health disorder classification on Reddit. We construct a large annotated dataset, validating its reliability through statistical judgmental analysis and topic modeling. Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches. RoBERTa achieved the highest classification performance, with a 99.54% F1 score on the hold-out test set and a 96.05% F1 score on the external test set. Notably, LSTM models augmented with BERT embeddings proved highly competitive, achieving F1 scores exceeding 94% on the external dataset while requiring significantly fewer computational resources. These findings highlight the effectiveness of transformer-based models for real-time, scalable mental health monitoring. We discuss the implications for clinical applications and digital mental health interventions, offering insights into the capabilities and limitations of state-of-the-art NLP methodologies in mental disorder detection.

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