Show simple item record

dc.contributor.authorThelwall, Mike
dc.date.accessioned2017-08-31T09:07:28Z
dc.date.available2017-08-31T09:07:28Z
dc.date.issued2018-02-14
dc.identifier.issn1468-4527en
dc.identifier.doi10.1108/OIR-05-2017-0139
dc.identifier.urihttp://hdl.handle.net/2436/620633
dc.description.abstractPurpose: To test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females. Design: This paper uses datasets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females. Findings: Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis. Research limitations: Only one lexical sentiment analysis algorithm was used. Practical implications: Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results. Originality/value: This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.
dc.language.isoenen
dc.publisherEmeralden
dc.relation.urlhttps://www.emeraldinsight.com/doi/full/10.1108/OIR-05-2017-0139en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSentiment analysisen
dc.subjectGenderen
dc.subjectBig dataen
dc.subjectGender biasen
dc.titleGender bias in sentiment analysisen
dc.typeJournal article
dc.identifier.journalOnline Information Reviewen
dc.date.accepted2017-08-23
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUoW310817MTen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2017-12-01en
dc.source.volume42
dc.source.issue1
dc.source.beginpage45
dc.source.endpage57
refterms.dateFCD2018-12-05T13:40:59Z
refterms.versionFCDAM
refterms.dateFOA2017-12-01T00:00:00Z
html.description.abstractPurpose: To test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females. Design: This paper uses datasets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females. Findings: Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis. Research limitations: Only one lexical sentiment analysis algorithm was used. Practical implications: Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results. Originality/value: This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.


Files in this item

Thumbnail
Name:
GenderBiasInSentimentAnalysisP ...
Size:
929.8Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

http://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/