HUMAN OPINION DYNAMICS FOR STRESS DETECTION USING MACHINE LEARNING

This book explores the intersection of human opinion dynamics and machine learning for stress detection, presenting a comprehensive framework for understanding how computational models can effectively analyze psychological stress patterns. It delves into the intricate relationship between human emotions, behavioral responses, and machine learning techniques, offering a robust approach to mental health assessment in both clinical and non-clinical settings.The book introduces fundamental concepts of human opinion dynamics, explaining how opinions, emotions, and psychological states evolve in social and digital environments. By integrating these concepts with cutting-edge machine learning algorithms, it provides insights into how stress levels can be detected, monitored, and predicted using computational methods. Various supervised, unsupervised, and deep learning techniques are explored to highlight their potential in analyzing physiological and psychological markers of stress.