Previously submitted to: JMIR Mental Health (no longer under consideration since Oct 14, 2025)
Date Submitted: Oct 11, 2025
Open Peer Review Period: Oct 14, 2025 - Oct 14, 2025
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Development of an AI-Integrated Online Counseling and Self-Improvement Platform for Mental Health Support
ABSTRACT
Background:
Mental health issues such as stress, anxiety, and depression are increasingly prevalent worldwide. However, access to professional counseling remains limited, especially in low-resource countries like Sri Lanka. Existing digital mental health tools often rely on internet connectivity and high-end hardware, creating accessibility barriers.
Objective:
This study aims to design and evaluate an AI-based counseling chatbot that operates offline using a lightweight TF-IDF model, providing accessible, stigma-free mental health support.
Methods:
A dataset of 1,872 mental health-related user expressions was collected from counseling transcripts, affective lexicons, and peer support forums. Text preprocessing included tokenization, stopword removal, and lemmatization. The TF-IDF model was implemented to classify user intents and generate context-appropriate responses from a curated response bank based on cognitive behavioral therapy principles. The model’s performance was evaluated using accuracy, relevance rating, and user feedback.
Results:
The chatbot achieved 91.2% accuracy in intent classification, with an average response time of 0.19 seconds and a memory footprint of only 2MB. User evaluation with 10 participants indicated a mean response relevance score of 4.3/5, and 80% reported reduced anxiety after chatbot interaction.
Conclusions:
The proposed TF-IDF-based chatbot demonstrates that lightweight AI systems can provide effective and empathetic mental health support in low-resource environments. Future work includes expanding to multilingual support and integrating with hybrid human-AI counseling frameworks.
Citation
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