Psychological Distress Detection and Classification

This research paper introduces an innovative method for stress detection and classification by utilizing a combination of machine learning, deep learning techniques, and BERT. Today with the advancement of technology and techniques like artificial intelligence and machine learning various manual tasks can be replaced very easily. Hence in this research paper, we have proposed various machine learning algorithms, deep neural networks, and BERT pre-trained models for the detection of stress based on their social media posts and then classifying their stress levels based on their responses to the universally accepted DASS(Depression Anxiety Stress Scales) questionnaire. This study aims to develop an effective system that can accurately detect and classify stress levels based on textual data. The results reflect that our system outperforms existing methods for stress detection and classification and proposes a refined approach for the classification of stress levels. This system, therefore, has potential applications in various domains, including mental health diagnosis, social media monitoring, and personalized stress management.