Mind Ease: A Web-Based AI Chatbot for Personalized Mental Health Support Using NLP and Sentiment Analysis

Abstract

The prevalence of mental health issues among people of all ages continues to increase, and timely mental health assistance remains challenging due to various factors such as social stigma, time constraints, and a shortage of mental health resources. This article talks about Mind Ease, a web-based chatbot that uses NLP and mood analysis to help people with their mental health in a personalized way. The proposed system enables realtime text-based conversations. The chatbot can pick up on emotions and respond to them in a supportive manner. To enhance the accuracy of mood recognition a LSTM is utilized. This model is good at capturing contextual information and linguistic dependencies in user inputs. It provides individual feedback, self-care advise and coping strategies based on the emotions that it identifies to enhance emotional health. Mind Ease also has a mood-tracing screen that provides weekly and monthly analytical charts that demonstrate user’s emotional patterns. This will allow them to be more aware of themselves and monitor their progress. A crisis detection system is also integrated for identifying sensitive and high-risk talks and providing the appropriate type of support. Besides, user authentication and interaction logs can provide a basic degree of user personalization, improving the overall user experience. The proposed system highlights the potential of AI-powered conversational systems to deliver 24/7, easily accessible, and emotionally intelligent mental health assistance via a web-based interactive environment.