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@article{186355,
        author = {Abinaya M and Aswin Kumar P and Bharath Kumar M K and Prabagaran A},
        title = {MENTAL HEALTH ANALYSIS FOR JUVENILE SCHOOL STUDENTS USING CHATBOT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {629-635},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186355},
        abstract = {This paper presents the design and development of an AI-driven Chatbot System aimed at providing a confidential, easily accessible, and preliminary screening tool for the mental health analysis of juvenile school students. Addressing the limitations of traditional counselling, the proposed system integrates a multi-modal input interface (text and voice/speech) with advanced Natural Language Processing (NLP) and Deep Learning (DL) models. The core functionality involves real-time Sentiment and Emotion Analysis on user inputs to accurately classify the user’s conversational state into low, moderate, or high-risk categories. The architecture incorporates Voice-to-Text (VTT) conversion for spontaneous interaction, a Bi-directional LSTM network for nuanced emotional detection, and a secure reporting module that facilitates real-time alerts to faculty counsellors for critical cases. By combining instantaneous support with adaptive psychological micro-interventions, the system enhances early detection mechanisms, promotes student wellness, and offers a scalable and non-judgmental platform for mental health support in educational institutions. This research significantly contributes to the field of AI-assisted juvenile mental healthcare by providing a robust, multi-modal, and preventive solution.},
        keywords = {},
        month = {November},
        }