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@article{198634,
        author = {Aditi Sadhankar and Sharayu Deote and Gayatri Bannagare and Sheetal Naik and Shital Takalkhede},
        title = {PSYTRACK - An AI-Hybrid Framework for Early Mental Health Screening Using Questionnaires and Emotion Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9767-9777},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198634},
        abstract = {Sentiment analysis is an important technique which helps detect the emotions and mental states in today's world of artificial intelligence and natural language processing. This project involves sentiment and mental health analysis system, which includes psychometric testing, sentiment classification, sarcasm detection and facial recognition to detect the anxiety and depression. This application starts by collecting information from users using psychometric assessment when the identified risk is medium or high then the facial recognition technique is used to get the non-verbal emotional signals. In this research sarcasm detection model checks the hidden emotional expressions that can tell real state of mind of the user. These several modules help to get textual, emotional, and visual signals which helps to detect the mental state of the user. This model uses clinical evaluation tools like PHQ-9 for depression and GAD-7 for anxiety which helps to generate a comprehensive evaluation report. By using psychometric assessment, sarcasm detection, facial recognition, and clinical evaluations helps determine whether consultation with a medical expert is needed. This application helps to early detection of mental health and supports technology- based approach toward good mental well-being.},
        keywords = {Sentiment analysis, Sarcasm detection, Psychometric assessment, Facial recognition, PHQ 9, GAD-7.},
        month = {April},
        }