Enhancing Emotion Detection in Chatbots for Improved Mental Health Support: Addressing Limitations in Mental Disorders and Emotion Detection

The increase in the adoption of AI-driven chatbots in mental health support, accurately detecting and responding to users' emotions is crucial for effective communication. This paper proposes a novel framework that enhances emotion detection in chatbots by leveraging BERT (Bidirectional Encoder Representations from Transformers) and ensemble learning techniques. Our approach addresses the inherent limitations in existing emotion detection systems, particularly when dealing with complex emotional states and mental health disorders. Using an array of tests, we show that our suggested model works better than conventional approaches, providing enhanced precision, resilience, and the ability to more effectively assist mental health therapies. This research contributes to the ongoing development of AI in mental health, providing a more nuanced and effective approach to emotion detection.