Transformer and Deep Learning-based Depression Detection from Low-Resource Language

Depression detection in low-resource languages, such as Bangla, is an emerging challenge in mental health research due to the limited availability of labeled datasets and NLP resources. Bangla, the seventh most spoken language globally, is used by over 230 million people, yet it remains underrepresented in natural language processing (NLP) research compared to high-resource languages like English. In this study, we present a Bangla depression detection dataset collected from social media comprising 18,612 text samples, with 8556 labeled as depressive and the remaining as non-depressive. To effectively classify depressive content, we explore multiple deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). Additionally, we evaluate state-of-the-art transformer-based models such as BanglaBERT, BanglaBERT-Base, ELECTRA, RoBERTa, and BERT. Machine learning models are also employed as baselines for comparison. Our experimental results show that transformer-based models outperform both deep learning and machine learning approaches, with BanglaBERT achieving the highest accuracy of 89.23%, followed closely by BiLSTM with 88%. These findings highlight the effectiveness of transformer-based architectures in depression detection for Bangla text, contributing to improved mental health analysis in low-resource language settings. This study emphasizes the need for further NLP advancements in Bangla to develop AI-driven solutions for early depression detection and intervention.