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dc.contributor.authorThelwall, Mike
dc.date.accessioned2016-07-04T15:10:55Z
dc.date.available2016-07-04T15:10:55Z
dc.date.issued2016-07-12
dc.identifier.citationThelwall, M. (2016) 'TensiStrength: Stress and relaxation magnitude detection for social media texts', Information Processing & Management, 53 (1) pp. 106-121
dc.identifier.issn0306-4573
dc.identifier.doi10.1016/j.ipm.2016.06.009
dc.identifier.urihttp://hdl.handle.net/2436/615485
dc.description.abstractComputer 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.
dc.language.isoen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0306457316302321
dc.subjectsentiment analysis
dc.subjectstress detection
dc.subjectrelaxation detection
dc.titleTensiStrength: Stress and relaxation magnitude detection for social media texts
dc.typeJournal article
dc.identifier.journalInformation Processing & Management
dc.date.accepted2016-06-30
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.project636160-2
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0
rioxxterms.licenseref.startdate2018-01-31
dc.source.volume53
dc.source.issue1
dc.source.beginpage106
dc.source.endpage121
refterms.dateFCD2018-10-19T09:23:24Z
refterms.versionFCDAM
refterms.dateFOA2018-01-31T00:00:00Z
html.description.abstractComputer 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.


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