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Profiling Psychopathic Behavior Using Machine Learning

Psychopathy is a complex personality disorder characterized by persistent deficits in empathy and manipulative behavior. Traditional diagnostic methods often rely on subjective clinical assessments, which are susceptible to deception. This research proposes an objective, non-invasive computational framework for profiling psychopathic traits using Natural Language Processing (NLP) and Machine Learning. We developed a systematic pipeline utilizing transcribed interviews from confirmed criminal psychopaths and a balanced control group. To address data sparsity and noise, we employed the Dynamic Variance Thresholding (DyVaT) algorithm to construct a semantically dense vocabulary of over 1,300 features. The methodology integrates advanced preprocessing, TF-IDF vectorization, and synonym-based data augmentation to enhance model generalization. Among the evaluated classifiers, a Linear Support Vector Machine (SVM) achieved the highest performance, with an accuracy of 0.8081 and an F1-score of 0.7957. Our findings demonstrate the efficacy of linguistic biomarkers and feature importance analysis in distinguishing psychopathic speech patterns. This study provides a scalable methodology for early screening and diagnostics, with significant implications for forensic psychology, security, and ethical AI deployment in mental health.