AI Chatbot for Mental Health Using Natural Language Processing and Safety-Aware Dialogue Management

Abstract

Mental health disorders such as anxiety, stress, depression and emotional distress are increasing rapidly among students and working professionals. Limited availability of mental health professionals, social stigma and high consultation costs prevent many individuals from seeking timely help. Conversational artificial intelligence offers a scalable and privacy-preserving approach for providing preliminary emotional support and guidance.

This paper presents the design and implementation of an AI-based mental health chatbot that uses natural language processing, sentiment and emotion analysis, and safety-aware dialogue management to provide empathetic and context-aware responses. The system detects emotional states such as stress, sadness, anxiety and crisis indicators, and provides supportive responses, self-help strategies and helpline guidance when required. The chatbot is implemented using Python, machine-learning-based intent and risk classifiers, and a lightweight web interface for real-time interaction. Experimental evaluation shows that the proposed system achieves high intent classification accuracy and reliable crisis detection while maintaining low response latency. The proposed solution demonstrates the feasibility of deploying conversational AI as an assistive and scalable mental health support tool.