Mind Full Mingle – Mental Health Chat Bot

Mental health problems are becoming more widespread and require innovative solutions that provide timely and effective support. This research aims to develop a mental health chatbot using Python and Flask designed to facilitate conversations and provide personalized mental health recommendations. The primary goal is to create an interface where users can express their feelings and receive appropriate guidance, thereby improving mental well-being. The methodology involves implementing natural language processing (NLP) techniques to accurately understand and respond to user input. The chatbot uses a combination of pre-trained language models and customized algorithms to ensure relevant and empathetic responses. Initial results indicate high levels of user engagement and satisfaction, with the chatbot effectively identifying common mental health issues and offering appropriate recommendations. The main contribution of this research lies in the development of an accessible, scalable and effective mental health promotion tool that can be integrated into various platforms to reach a wider audience. This project highlights the potential of AI-driven solutions to address critical societal issues such as mental health and provides a foundation for future advancements in this area.

Authors

Institutions

Publication Details

Journal
International Research Journal on Advanced Engineering and Management (IRJAEM)
Published
2024-10-18
DOI
https://doi.org/10.47392/irjaem.2024.0467
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mind Full Mingle – Mental Health Chat Bot

Prashant Kumar, Sd. Rafiya, Y. B. Devayani, P. Sai Srideep et al.
International Research Journal on Advanced Engineering and Management (IRJAEM)
Digital Mental Health Interventions
article

Mind Full Mingle – Mental Health Chat Bot

Prashant Kumar, Sd. Rafiya, Y. B. Devayani, P. Sai Srideep, B. Y. Saradhi
article en

Abstract

Mental health problems are becoming more widespread and require innovative solutions that provide timely and effective support. This research aims to develop a mental health chatbot using Python and Flask designed to facilitate conversations and provide personalized mental health recommendations. The primary goal is to create an interface where users can express their feelings and receive appropriate guidance, thereby improving mental well-being. The methodology involves implementing natural language processing (NLP) techniques to accurately understand and respond to user input. The chatbot uses a combination of pre-trained language models and customized algorithms to ensure relevant and empathetic responses. Initial results indicate high levels of user engagement and satisfaction, with the chatbot effectively identifying common mental health issues and offering appropriate recommendations. The main contribution of this research lies in the development of an accessible, scalable and effective mental health promotion tool that can be integrated into various platforms to reach a wider audience. This project highlights the potential of AI-driven solutions to address critical societal issues such as mental health and provides a foundation for future advancements in this area.

International Research Journal on Advanced Engineering and Management (IRJAEM)Vol. 2(10)
Sri Ramachandra Institute of Higher Education and Research (IN), Cyber University (JP)
Openalex Percentile: Top 79%
Digital Mental Health Interventions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.