Application of Artificial Intelligence in Chatbot and Social Media Community for a Mental Health Monitoring Web-Application
Mental health is a major challenge for modern society affecting people of working age and their families, jobs, and communities. To provide continuous emotional support, personalized assistance, and remote mental health tracking, chatbots and social media platforms are increasingly using Artificial Intelligence (AI). There is little or no previous literature that has documented a fully functional mental health monitoring web-app that uses AI to provide all these features: chatbot, e-community, video conferencing, and email automation. Chatbots are software systems that offer various interactive online services, including people with mental health needs and have been successfully integrated into the field of mental healthcare. Similarly, social media can help monitor the mental health situation by extracting information from posts for sentiment analysis as people often post their feelings on social media, so analysing these posts can reveal their mood, emotion, cognition, or mental state. This paper solves the problem sentiment by using the MERN stack to develop an interactive mental health monitoring application which features: social media community, chatbot named ‘Dave the HappyBot’, Email automation and video conferencing. The results showed that among the 13 users of the web-app from a performance indices class of A, B, C, D, and E, majority indicated that they felt ‘happier’ and ‘somewhat mentally relieved’ after interacting with the Chatbot. The A class recorded 18% responses, B class obtained 31%, the C class got 19%, the D class achieved 26%, and the E class gained 6%. The users were open to recommending the App to their peer and meeting their psychologist for constant mental health monitoring sessions. This paper can be further improved documenting the developments of web-app with more users who possess higher knowledge of software development and have a history of mental health struggle. Higher utility of the MERN stack would also be an improvement.
Authors
- Obumneme Obiajulu Umeonwuka (ORCID: https://orcid.org/0000-0002-9548-6626)
- Zakka Augustine (ORCID: https://orcid.org/0000-0002-7366-7071)
- David Oluwaseun Kajewole (ORCID: https://orcid.org/0000-0002-7471-2719)
- Philimon Bala Sambo
- Marycynthia Chidinma Umeonwuka
- Salome Danjuma Mammanijabs
Institutions
- Rochester Institute of Technology (US)
- Federal University of Technology Owerri (NG)
- Abubakar Tafawa Balewa University (NG)
- University of Johannesburg (ZA)
- Kaduna State University (NG)
- University of Hradec Králové (CZ)
Publication Details
- Journal
- ABUAD Journal of Engineering Research and Development (AJERD)
- Published
- 2025-10-02
- DOI
- https://doi.org/10.53982/ajerd.2025.0803.04-j
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00