TensiStrength: Stress and relaxation magnitude detection for social media texts

TL;DR

TensiStrength is a lexical rule-based system designed to detect stress and relaxation levels in social media texts, offering a practical alternative to machine learning methods for stress analysis.

Contribution

The paper introduces TensiStrength, a novel lexical and rule-based approach for detecting stress and relaxation in social media texts, especially in transportation contexts.

Findings

TensiStrength performs slightly better than sentiment analysis in stress detection.

Effectiveness varies with the nature of tweets, especially those rich in stress-related terms.

Machine learning methods can outperform TensiStrength but may be less practical in certain applications.

Abstract

Computer systems need to be able to react to stress in order to perform optimally on some tasks. This article describes TensiStrength, a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation, particularly in the context of transportation. It is slightly more effective than a comparable sentiment analysis program, although their similar performances occur despite differences on almost half of the tweets gathered. The effectiveness of TensiStrength depends on the nature of the tweets classified, with tweets that are rich in stress-related terms being particularly problematic. Although generic machine learning methods can give better performance than TensiStrength overall, they exploit topic-related terms in a way that may be undesirable in practical applications and that may not work as well in more focused contexts. In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.