Sentimental Analyzer for Depression Using Machine Learning Approaches

Mental stress and depression are prevalent psychological conditions that affect millions of individuals worldwide. Early detection and intervention are crucial for effective management and treatment. With advancements in technology, particularly in the field of computer vision and machine learning, there has been growing interest in utilizing facial input for the detection of mental stress and depression. This abstract presents a concise overview of recent research in this area. Facial input-based detection methods leverage the analysis of facial expressions, movements, and features to infer an individual’s emotional state. Various techniques have been explored, including traditional computer vision algorithms and deep learning approaches. These methods aim to capture subtle changes in facial expressions associated with mental stress and depression, such as sadness, fatigue, and withdrawal.