A Next-Generation NLP Framework for Psychological Behavior Analysis Based on State-of-the-art Language Model

Psychological behavior analysis is critical in mental health diagnosis, but typical machine learning approaches frequently struggle to appropriately read complex psychological cues. This research fills that gap by presenting a new framework that combines Large Language Models with psychological theory mapping to improve behavior classification. The method tries to close the gap between machine learning performance and clinical relevance by adding well-known psychological theories, resulting in high accuracy and interpretable findings for real-world applications. The proposed model has been assessed on different types of standard and benchmarked dataset where the accomplished outcome exhibited notable performance gain of proposed model in contrast to existing system. The proposed model is recorded with 93.1% accuracy and 92.9% of F1-Score in contrast to several potential approaches e.g. Support Vector Machine, Random Forest, Convolution Neural Network. The outcome states the reliability of the model towards identifying the psychological behaviour offering interpretable and faster clinical prediction leveraged for real-world capabilities. The study contributes to a unified framework that simplifies the diagnostic process of mental health with elevated quantified performance being recorded.