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 contributes 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.