Chatbot for Mental health support using NLP

Abstract

Mental health issues are a growing concern worldwide, and seeking support for these issues can be difficult due to various reasons. Chatbots have emerged as a promising solution to provide accessible and confidential support to individuals facing mental health issues. With recent advances in technology, digital interventions designed to supplement or replace in-person mental health services have proliferated, including the emergence of mental health chatbots that claim to provide assistance through automated natural language processing (NLP) therapeutic approaches. A chatbot can be described as a computer program capable of providing intelligent answers to user input by understanding natural language using one or more NLP techniques. In this study, we discuss the use of NLP in psychotherapy and compare the responses provided by chatbots to a set of predefined user inputs related to well-being and mental health queries and compare existing systems. A general analysis was performed. The general approach to building such chatbots includes basic NLP techniques such as word embedding, sentiment analysis, sequence-by-sequence models, and attention mechanisms. We also looked at Mental Ease, a mobile app that uses NLP technology not only to provide conversational assistance but also to tool up useful features for maintaining mental health. Incorporating mental health assessment tools into the chatbot interface, it can help patients cope with mild anxiety and depression alongside conventional therapy. It can also overcome some barriers to mental health, such as waiting lists and geographical barriers to face-to-face consultations.