Mental Health Support Chatbot
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Mental Health Support Chatbot
Authors:
Aman Singh, Anurag Yesansure, Priya Mourya and Vijay Singh
Shobha Bamane
Department of Computer Engineering
ISB&M College of Engineering, Pune-412115, Maharashtra, India
ABSTRACT
In today's busy, high-pressure workplaces, mental health conditions like stress, anxiety, and depression are on the rise. Due to the high expense of treatment, geographic limitations, and social stigma, many people do not receive the proper support. This study offers a comprehensive framework for a cost-effective, AI-powered chatbot that promotes mental health and is accessible 24/7 to assist users with their unique mental wellness concerns.
The chatbot makes use of advanced machine learning and natural language processing capabilities with the aid of OpenCV for facial emotion recognition and Langchain-integrated large language models (LLMs). The chatbot interprets user text input and facial expressions to customize responses based on emotional state, ensuring conversations that are human-like, sympathetic, and contextually aware.
In contrast to conventional models that rely on fixed datasets, a powerful LLM empowers the chatbot to create contextually and emotionally aware responses in real-time without the need for static dataset training. The use of real-time analytics and ongoing monitoring enables user-level feedback and assessment of interactions, enhancing its responsiveness and adaptability. With the capacity to identify emotions such as joy, anger, or sadness, the chatbot can deliver appropriate support and guidance. An intuitive interface boosts accessibility and offers valuable insights for individuals seeking mental health assistance.
This structured, privacy-focused approach offers a judgment-free environment where individuals can openly discuss their mental health challenges. The key advantages of the system include its timely availability, emotional awareness, and ability to scale. Future enhancements will focus on improving emotion detection, facilitating long-term contextual dialogues, and integrating voice communication to deliver a more engaging and interactive support experience.
Consequently, a Langchain and real-time AI technology-based chatbot system has the potential to revolutionize the mental health care sector by delivering continuous, scalable, and highly personalized support.
Keywords: Natural Language Processing (NLP), OpenCV, Emotion Detection, AI-Powered Chatbots, Langchain, Text-Based Interactions, Stigma-Free Therapy, Personalized Support, Predictive Mental Health Support.