Authors :
Dr. V. Sathiyasuntharam; Neha Kumari; Saloni Verma
Volume/Issue :
Volume 10 - 2025, Issue 11 - November
Google Scholar :
https://tinyurl.com/4u9mn9t
Scribd :
https://tinyurl.com/3j2c6v5d
DOI :
https://doi.org/10.38124/ijisrt/25nov376
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
This project presents the development of an AI-powered mental health chatbot aimed at providing instant
emotional support and mental well-being guidance through intelligent conversation. The system is built using Natural
Language Processing (NLP), Machine Learning, and sentiment analysis techniques, implemented through Python (with
libraries like NLTK, TextBlob, and Hugging Face Transformers) and integrated via a Flask-based web interface.
Keywords :
Natural Language Processing (NLP), Machine Learning, and Sentiment Analysis Techniques, Implemented Through Python (with Libraries Like NLTK, TextBlob, and Hugging Face Transformers).
References :
- H. Pontes, B. Schivinski, C. Sindermann, M. Li, B. Becker et al., “Measurement and conceptualization of gaming disorder according to the world health organization framework: The development of the gaming disorder test,” International Journal of Mental Health and Addiction, vol. 19, no. 2, pp. 508–528, 2021.
- Y. Ransome, H. Luan, I. Song, D. Fiellin and S. Galea, “Association of poor mental-health days with COVID-19 infection rates in the US,” American Journal of Preventive Medicine, vol. 62, no. 3, pp. 326–332, 2022.
- R. Levant, M. Gregor and K. Alto, “Dimensionality, variance composition, and development of a brief form of the duke health profile, and its measurement invariance across five gender identity groups,” Psychology & Health, vol. 37, no. 5, pp. 658–673, 2022.
- S. Zhang, T. Gong, H. Wang, Y. Zhao and Q. Wu, “Global, regional, and national endometriosis trends from 1990 to 2017,” Annals of the New York Academy of Sciences, vol. 1484, no. 1, pp. 90–101, 2021.
- J. Campion, A. Javed, C. Lund, N. Sartorius, S. Saxena et al., “Public mental health: Required actions to address implementation failure in the context of COVID-19,” The Lancet Psychiatry, vol. 9, no. 2, pp. 169–182, 2022.
- B. Williamson, K. Gulson, C. Perrotta and K. Witzenberger, “Amazon and the new global connective architectures of education governance,” Harvard Educational Review, vol. 92, no. 2, pp. 231–256, 2022.
- A. Chan and M. Hone, “User perceptions of mobile digital apps for mental health: Acceptability and usability-an integrative review,” Journal of Psychiatric and Mental Health Nursing, vol. 29, no. 1, pp. 147– 168, 2022.
- T. Furukawa, A. Suganuma, E. Ostinelli, G. Andersson, C. Beevers et al., “Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: A systematic review and component network meta-analysis using individual participant data,”The Lancet Psychiatry, vol. 8, no. 6, pp. 500–511, 2021.
- E. Lattie, C. Stiles-Shields and A. Graham, “An overview of and recommendations for more accessible digital mental health services,” Nature Reviews Psychology, vol. 1, no. 2, pp. 87–100, 2022.
- J. Paay, J. Kjeldskov, E. Papachristos, K. Hansen, T. Jørgensen et al., “Can digital personal assistants persuade people to exercise,” Behaviour & Information Technology, vol. 41, no. 2, pp. 416–432, 2022.
- K. Nirala, N. Singh and V. Purani, “A survey on providing customer and public administration based services using AI: Chatbot,” Multimedia Tools and Applications, vol. 81, no. 1, pp. 22215–22246, 2022.
- A. Adikari, D. De Silva, H. Moraliyage, D. Alahakoon, J. Wong et al., “Empathic conversational agents for real-time monitoring and co-facilitation of patient-centered healthcare,” Future Generation Computer Systems, vol. 126, no. 1, pp. 318–329, 2022.
- N. Kazantzis and A. Miller, “A comprehensive model of homework in cognitive behavior therapy,” Cognitive Therapy and Research, vol. 46, no. 1, pp. 247–257, 2022.
This project presents the development of an AI-powered mental health chatbot aimed at providing instant
emotional support and mental well-being guidance through intelligent conversation. The system is built using Natural
Language Processing (NLP), Machine Learning, and sentiment analysis techniques, implemented through Python (with
libraries like NLTK, TextBlob, and Hugging Face Transformers) and integrated via a Flask-based web interface.
Keywords :
Natural Language Processing (NLP), Machine Learning, and Sentiment Analysis Techniques, Implemented Through Python (with Libraries Like NLTK, TextBlob, and Hugging Face Transformers).