Detection of Strength and Causal Agents of Stress and Relaxation for Tweets

Abstract

The ability to detect human stress and relaxation is central for timely diagnosing stress-related diseases, ensuring customer satisfaction in services and managing human-centric applications such as traffic management. Traditional methods employ stress measuring scales or physiological monitoring which may be intrusive and inconvenient. Instead, the ubiquitous nature of social media can be leveraged to identify stress and relaxation. In this PhD research, we introduce an improved method to detect expressions of stress and relaxation in social media content. It uses word sense vectors for word sense disambiguation to improve the performance of the first ever lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that TensiStrength with word sense disambiguation performs better than the original TensiStrength and state-of-the-art machine learning methods in terms of Pearson's correlation and accuracy. We also suggest a novel, word-vector based approach for detecting causes of stress and relaxation in social media content.