A HYBRID TRANSFORMER ARCHITECTURE FOR MULTI-CLASS MENTAL ILLNESS PREDICTION USING SOCIAL MEDIA TEXT

This project presents a hybrid transformer-based architecture for the multiclass classification of mental illnesses using social media text. Four hybrid models have been developed and evaluated: BERT + CNN, RoBERTa + BiLSTM, GPT2 + BiLSTM, and DistilBERT + CNN. These models are trained to identify and classify mental health conditions into these categories: Anxiety', 'Bipolar', 'Depression', 'Normal', 'Personality disorder', 'Stress', 'Suicidal'. The project uses preprocessed textual data from social media platforms and leverages contextual embeddings and sequential learning to improve classification accuracy. A web-based application is developed using Flask, with modules for user registration, login, prediction, and logout. The system allows users to input text and receive a predicted mental health status. The backend is supported by transformer models and deep learning architectures, ensuring efficient prediction and scalability. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to compare models and determine the best-performing hybrid.

Keywords: Mental illness, Social media, Transformer, BERT, CNN, BiLSTM, RoBERTa, GPT2, DistilBERT, Flask