NAÏVE BAYES-BASED EMOTION RECOGNITION AND RESPONSE GENERATION FOR AI MENTAL HEALTH THERAPY CHATBOTS FOR EDUCATIONAL APPLICATIONS

Abstract

Millions of people throughout the world struggle with mental health issues, and many of them encounter obstacles when trying to get timely, reasonably priced, and private therapeutic support. The study suggests an AI-Driven Emotional Support and Therapy Chatbot that uses machine learning and natural language processing (NLP) to have human-like, sympathetic discussions and offer users individualized mental health support. The chatbot can provide evidence-based interventions including journaling prompts, mindfulness exercises, cognitive behavioral therapy (CBT), and stress management techniques because it is built to detect emotional indicators from text-based inputs. The chatbot makes users feel heard and understood by offering ongoing emotional support through sympathetic dialogue in addition to therapeutic recommendations. In order to identify indications of severe mental distress and direct users toward professional assistance when needed, a safety layer is included. Strict data privacy and ethical standards are upheld as the system is trained on a variety of datasets to guarantee inclusivity and cultural sensitivity. Over time, the ability to learn continuously improves therapeutic accuracy and conversational depth. This solution has the potential to help marginalized communities and lessen the stigma attached to obtaining mental health care because it is affordable and available around-the-clock. The suggested chatbot exemplifies the potential of integrating AI and psychology to democratize therapeutic and emotional support.