2.50
Hdl Handle:
http://hdl.handle.net/2436/620633
Title:
Gender bias in sentiment analysis
Authors:
Thelwall, Mike ( 0000-0001-6065-205X )
Abstract:
Purpose: 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.
Publisher:
Emerald
Journal:
Online Information Review
Issue Date:
Dec-2017
URI:
http://hdl.handle.net/2436/620633
Additional Links:
http://www.emeraldinsight.com/loi/oir
Type:
Article
Language:
en
ISSN:
1468-4527
Appears in Collections:
Statistical Cybermetrics Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorThelwall, Mikeen
dc.date.accessioned2017-08-31T09:07:28Z-
dc.date.available2017-08-31T09:07:28Z-
dc.date.issued2017-12-
dc.identifier.issn1468-4527en
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.en
dc.language.isoenen
dc.publisherEmeralden
dc.relation.urlhttp://www.emeraldinsight.com/loi/oiren
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.typeArticleen
dc.identifier.journalOnline Information Reviewen
dc.date.accepted2017-08-
rioxxterms.funderInternalen
rioxxterms.identifier.projectUoW310817MTen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2017-12-01en
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