DEEP LEARNING-BASED MENTAL HEALTH DETECTION USING FINE-TUNED BERT: A MULTICLASS TEXT CLASSIFICATION APPROACH

Mental health disorders, including anxiety, bipolar disorder, and suicidal tendencies, significantly affect individual well-being and necessitate timely detection for effective intervention. Traditional assessment methods, such as clinical evaluations and self-reported surveys, are often time-consuming and subjective. This paper introduces a deep learning-based approach utilizing a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model for multi-class mental health classification through textual analysis. The system classifies text into four categories—depression, anxiety, stress, and normal using advanced natural language processing (NLP) techniques. It features a real-time interface developed using Streamlit, offering an accessible and intuitive tool for clinicians and users. Evaluation on the DAIC-WOZ and Reddit-based datasets yields an accuracy of 93%, demonstrating the model’s strong performance. A user-friendly Graphical User Interface (GUI) was developed to facilitate real-time classification, allowing users to input text and receive immediate feedback. Evaluation through classification metrics confirmed the model’s effectiveness. The proposed system offers a scalable and automated approach to mental health assessment, supporting early intervention and reducing the burden on healthcare systems. Keywords: Mental Health, Deep Learning, BERT, Text Classification, Streamlit, NLP