Cognitive Pattern Analyzer

Abstract

How people interpret what happens to them matters enormously for their mental wellbeing. When those interpretations are consistently skewed — treating setbacks as proof of worthlessness, or minor inconveniences as impending disasters — clinicians describe the resulting habit as a cognitive distortion. Decades of psychiatric research tie these distortions to worsening anxiety, chronic stress, depressive episodes, and eroded self-regard. Diagnosing them conventionally means structured clinical interviews, paper-based inventories, and trained observation — procedures that become unworkable the moment a practitioner faces thousands of text records rather than a single patient. Digital therapy platforms and mental health forums now produce exactly that kind of textual volume, and no manual workflow can keep pace. This paper responds to that gap by presenting a hybrid framework that fuses natural language processing with supervised machine learning to spot and label distortions in free-form writing. The pipeline extracts the linguistic fingerprints of patterns including overgeneralization, catastrophizing, and black-or-white reasoning directly from what user’s type.