Depression Detection in Social Media Using NLP and Hybrid Deep Learning Models

One type of feeling that possesses a detrimental effect on people's day-to-day lives is depression. Globally, the number of persons experiencing long-term sentiments is rising annually. Many psychiatrists find it difficult to recognize mental disease or unpleasant emotions in patients before it's too late to improve treatment. Finding depression in individuals quickest possible time represents one of the most difficult problems. To create tools for diagnosing depression, researchers are employing NLP to examine written content shared on social media sites. Traditional techniques frequently have problems with scalability and poor precision. To overcome the drawbacks of the prior methods, it is suggested to introduce an improved depression detection system based on the RoBERTa (Robustly optimized BERT approach) and BiLSTM (Bidirectional Long Short-Term Memory) approach. This proposed work aims is to take advantage of the contextualized word embeddings from RoBERTa and the sequential learning properties of BiLSTM to determine depression from social media text. The technique is innovative because it combines the use of BiLSTM to accurately describe the temporal patterns of text sequences with RoBERTa to capture subtle linguistic aspects. It removes stopwords and punctuations form the input data to provide clean data to the model for processing. The system illustrates preference over the existing models as they achieve a 99.4 % accuracy, 98. 5% precision, 97. 1% recall, and 97. 3% F1 score. Thus, these results clearly highlight the effectiveness of the combination of the proposed technique with the traditional method in identifying depression with more accuracy and less variance. The proposed method is implemented using python.

@article{Padmaja2025,
  title     = {Depression Detection in Social Media Using NLP and Hybrid Deep Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {S M Padmaja and Sanjiv Rao Godla and Janjhyam Venkata Naga Ramesh and Elangovan Muniyandy and Pothumarthi Sridevi and Yousef A.Baker El-Ebiary and David Neels Ponkumar Devadhas},
  doi       = {10.14569/IJACSA.2025.01602106},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602106}
}