Authors : Tanu Chauhan; Abhishek Verma

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/ycxdbsh8

DOI : https://doi.org/10.38124/ijisrt/26aug324

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Depression is a widespread and frequently under-diagnosed mental health condition, and delays in identification are associated with poorer long-term outcomes. This paper presents a hybrid screening framework that combines a validated questionnaire to the Patient Health Questionnaire-9 (PHQ-9), with linguistic feature extraction from short free-text journal entries to generate a composite depression-risk score. Unlike approaches that rely on a single data modality, the proposed system fuses structured clinical scoring with textual indicators associated with depressive symptomatology in prior computational-linguistics research, such as elevated first-person pronoun usage, negation patterns, and lexical markers of sadness, anhedonia, and worthlessness. A prototype web application implementing this framework was developed, comprising a screening interface, a relational database schema for longitudinal storage, and an analytics dashboard for aggregate monitoring. This paper describes the system architecture, the feature-fusion methodology, the underlying database design, and a discussion of how the prototype's rule-based linguistic module can be replaced with a trained supervised classifier (e.g., Support Vector Machine, Random Forest, or a fine-tuned transformer model) in future work. The framework is intended as a low-cost, scalable screening aid to support — not replace — clinical [1] evaluation.

Keywords : Depression Recognition; Machine Learning; Natural Language Processing; PHQ-9; Mental Health Screening; Text Classification; Linguistic Analysis.

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