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@article{180953,
        author = {Diya S Thange and Monika M M and Ganavi R D and Bhanupriya V P and Rohan D Joel},
        title = {AI-Based Early Detection of Mental Health Conditions Through Textual Communication Patterns: A Literature Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3202-3209},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180953},
        abstract = {The rise of digital communication platforms has enabled the application of Artificial Intelligence (AI) for early detection of mental health conditions through Natural Language Processing (NLP) of textual data. This literature review synthesizes insights from 25 recent studies that leverage AI models to analyze communication patterns, focusing on indicators of depression, anxiety, stress, and mood disorders. Three core research directions are explored: NLP-based symptom recognition, transformer-based contextual language modeling, and behavioral metadata integration. NLP techniques utilize psycholinguistic cues, semantic features, and sentiment patterns to identify linguistic markers of mental health issues. Transformer architectures such as BERT and RoBERTa enhance contextual understanding, improving classification accuracy by capturing nuanced variations in expression. Multimodal frameworks further strengthen detection by incorporating temporal, behavioral, and interaction- based signals, enabling a more holistic assessment of mental well-being. While these systems demonstrate strong potential for early diagnosis and continuous monitoring, challenges remain in model interpretability, data privacy, ethical considerations, and generalization across diverse populations and languages. This paper reviews current advancements, identifies limitations, and outlines future directions for ethically grounded, scalable, and clinically applicable AI-based mental health monitoring systems.},
        keywords = {MArtificial Intelligence, Mental Health Monitoring, Natural Language Processing, Transformer Models, Communication Patterns, Sentiment Analysis, Behavioral Signal Analysis, Early Diagnosis, Ethical AI, Text Mining},
        month = {June},
        }