  Due to massive adoption of social media, detection of users' depression
through social media analytics bears significant importance, particularly for
underrepresented languages, such as Bangla. This study introduces a
well-grounded approach to identify depressive social media posts in Bangla, by
employing advanced natural language processing techniques. The dataset used in
this work, annotated by domain experts, includes both depressive and
non-depressive posts, ensuring high-quality data for model training and
evaluation. To address the prevalent issue of class imbalance, we utilised
random oversampling for the minority class, thereby enhancing the model's
ability to accurately detect depressive posts. We explored various numerical
representation techniques, including Term Frequency-Inverse Document Frequency
(TF-IDF), Bidirectional Encoder Representations from Transformers (BERT)
embedding and FastText embedding, by integrating them with a deep
learning-based Convolutional Neural Network-Bidirectional Long Short-Term
Memory (CNN-BiLSTM) model. The results obtained through extensive
experimentation, indicate that the BERT approach performed better the others,
achieving a F1-score of 84%. This indicates that BERT, in combination with the
CNN-BiLSTM architecture, effectively recognises the nuances of Bangla texts
relevant to depressive contents. Comparative analysis with the existing
state-of-the-art methods demonstrates that our approach with BERT embedding
performs better than others in terms of evaluation metrics and the reliability
of dataset annotations. Our research significantly contribution to the
development of reliable tools for detecting depressive posts in the Bangla
language. By highlighting the efficacy of different embedding techniques and
deep learning models, this study paves the way for improved mental health
monitoring through social media platforms.
