  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.
