AI Driven Mental Health Chatbot with Emotion Detection

Mental health issues such as stress, anxiety, and depression are increasing globally, yet access to timely professional support remains limited due to stigma, high costs, and shortage of therapists. This project proposes an AI-driven mental health chatbot with integrated emotion detection to provide users with real-time, empathetic, and accessible mental health support. The chatbot leverages Natural Language Processing (NLP) to understand user input, sentiment analysis and emotion recognition models to detect the user's emotional state, and machine learning algorithms to deliver context-aware, personalized responses. By incorporating emotion detection through text and, optionally, speech analysis, the system can adapt its tone, recommend relaxation exercises, or escalate critical cases for professional intervention. The chatbot will be accessible via web and mobile platforms, ensuring continuous availability and anonymity, thus encouraging users to openly express their feelings. Technologies employed include Python, TensorFlow for emotion recognition, NLP frameworks Flair and cloud deployment for scalability. The expected outcome is an intelligent virtual companion capable of offering empathetic conversations, emotional support, self-help resources, and early intervention cues, ultimately bridging the gap between individuals in need and mental health care services.