Unravelling Emotional Well-Being: Detecting Stress in Social Media Through Advanced Deep Learning Techniques

Stress has become an almost constant part of our modern lives, it occurs due to communication with people on social networks, at work, and in other aspects of life. It is recommended to detect stress in the course of which timely interventions and preliminary mental well-being assessment are possible. In this work, we present approaches for stress classification in social media text using the RoBERTa model. Stress detection is a critical application area for RoBERTa since the model is designed to capture contextual insights in textual data. Our strategy builds on the RoBERTa model to extract language and contextual features of stress from text messages. Since the proposed scenario combines datasets of Reddit comments and tweets, it shows an enhanced accuracy of stress detection. Experimental outcome supports the hypothesis that RoBERTa functions well as stress-detecting in social media conversations.