Abstract
The ability to automatically detect human stress and relaxation is crucial 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 the social media can be leveraged to identify stress and relaxation, since many people habitually share their recent life experiences through social networking sites. This paper introduces an improved method to detect expressions of stress and relaxation in social media content. It uses word sense disambiguation by word sense vectors to improve the performance of the first and only lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that incorporating word sense disambiguation substantially improves the performance of the original TensiStrength. It performs better than state-of-the-art machine learning methods too in terms of Pearson correlation and percentage of exact matches. We also propose a novel framework for identifying the causal agents of stress and relaxation in tweets as future work.