A Web Based Chatbot for Mental Health Support

Abstract

This study explores the development of a web-based chatbot designed to provide personalized mental health
therapy, addressing the challenge of accessing timely mental health interventions. The chatbot, developed using
a dataset of mental health-related FAQs, employs lemmatization, lowercasing, and duplication removal to
prepare data for analysis. Utilizing neural networks, particularly LSTM architecture, the machine learning
model shows a negative correlation between training epochs and loss magnitude, indicating improved
performance over time. The findings reveal the chatbot's high proficiency in delivering individualized care,
quick response, and relevant therapy recommendations. The study underscores the efficacy of chatbots in
mental health care, enhancing resource availability and addressing societal stigma, limited resources, and
geographical isolation issues. It recommends continuous updates to the chatbot’s knowledge base, therapy
suggestions, and conversational skills, ensuring its relevance and effectiveness in providing personalized mental
health care. This highlights the potential of advanced chatbots in revolutionizing mental health interventions
and support