Investigations in Psychological Stress Detection from Social Media Text using Deep Architectures

Abstract

Psychological stress is a feeling of mental stress and pressure. Within this context we try to understand the tweets which express psychological stress. Psychological stress detection is a complex task majorly because the stressed community contains mostly introverts. With the increase in people using social-media sites, the information related to mental health of the people extracted from their posts are increasing day-by-day. We use this information to design an AI enabled framework for automatic stress detection. However these data are noisy and complex, therefore deep learning based models are utilized for automatic extraction of features rather than manual extraction of features. Several deep learning based architectures including Multichannel CNN, CNN, GRU, Capsule network and BERT model are explored for solving this task of detecting tweets having mentions about mental stress. Experimental results on a standard Twitter dataset reveal that Multichannel CNN attains the best performance with accuracy of 97.5%, precision, recall and f-score values of 96.8%, 97.5% and 97.2%, respectively.