Deep Learning-Driven Real-Time Conversational Assistant for Mental Health Prediction and Support

This research presents a Deep Learning-Driven Real-Time Conversational Assistant designed to bridge the gap between digital expression and proactive mental health support. While modern users frequently share their emotional states on digital platforms, conventional sentiment analysis often fails to identify the nuanced linguistic patterns associated with acute psychological distress, such as chronic stress or clinical anxiety. To address this, the proposed framework utilizes state-of-the-art Transformer-based architectures (BERT and RoBERTa) to perform high-granularity emotion classification, effectively categorizing inputs into states of sadness, anger, stress, and anxiety. The system architecture integrates these detected emotional vectors into a Large Language Model (LLM), which is fine-tuned to generate empathetic, contextually relevant support responses. This ensures a human-centric interaction model that provides immediate comfort without overstepping into medical diagnosis. To enhance the assistant’s reliability, a Retrieval-Augmented Generation (RAG) module is incorporated that dynamically fetches localized mental health resources, including emergency helplines and nearby counseling centers, based on the user’s geographic coordinates. The final solution is deployed as a comprehensive web application featuring a real-time chat interface, a personalized mood dashboard, and longitudinal summaries of emotional trends. By combining deep learning-driven diagnostics with localized resource mapping, the paper offers a scalable, safe, and accessible intervention tool that empowers users to monitor their mental well-being and access professional support when critical emotional thresholds are met.