{"ID":2893340,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19511","arxiv_id":"2507.19511","title":"Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.19511","url_pdf":"https://arxiv.org/pdf/2507.19511v1","authors":"[\"Khalid Hasan\",\"Jamil Saquer\",\"Mukulika Ghosh\"]","published":"2025-07-17T04:58:31Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}