Chatbots for Mental Health: Leveraging LSTM and Seq2Seq Architectures to Enhance User Well-being

Abstract

Addressing mental health concerns is a growing global challenge, and seeking support can often be hindered by various barriers. Chatbots present a promising solution to offer accessible and confidential assistance to individuals grappling with mental health issues. With recent technological advancements, digital interventions have become prevalent. The aim of this research is to raise awareness about mental health while simultaneously working towards removing the societal stigma surrounding it. Thus, in this paper, we have created an integrated chatbot that is specifically geared towards mentally ill individuals. The chatbot responds empathetically which is built using a Sequence-to-Sequence (Seq2Seq) encoder-decoder architecture. Through user interaction analysis and sentiment tracking, the research evaluates the chatbot's capacity to detect early signs of mental health issues and assesses its impact on user well-being. Furthermore, the study investigates how users see confidentiality, privacy, and the general user experience when dealing with a chatbot for mental health on a website. The results provide fascinating details about the potential of web-based chatbots for mental health as proactive and approachable resources to promote people's mental health and well being.

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