“Mental Health Chatbot Using Natural Language Processing and Machine Learning Techniques”

Abstract

ABSTRACT: The rising demand for accessible mental health care has driven the development of AI-powered solutions capable of offering emotional support in real time. This research presents a web-based mental health chatbot that leverages Natural Language Processing (NLP) and machine learning to identify user emotions and intents, offering supportive responses through a hybrid system combining rule-based logic and BERT-based classification. The chatbot includes features such as CBT-style affirmations, self-assessment tests, journaling tools, and emergency support, ensuring both functionality and ethical user care. Evaluation on benchmark datasets demonstrated high accuracy in emotion (90%) and intent classification (85%), while user feedback confirmed the system's empathetic tone and practical utility. Although not a replacement for clinical care, the chatbot serves as a reliable and accessible first-line support tool. Future work aims to improve personalization, emotional adaptability, and multilingual capabilities. Keywords: Mental health chatbot, Natural Language Processing, emotional support, BERT, intent classification, emotion recognition, conversational AI, CBT, machine learning, ethical AI systems.