Gender bias in sentiment analysis

Gender bias in sentiment analysis PurposeThe purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.Design/methodology/approachThis paper uses data sets 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.FindingsMale 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/implicationsOnly one lexical sentiment analysis algorithm was used.Practical implicationsCare 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/valueThis is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Online Information Review Emerald Publishing

Gender bias in sentiment analysis

Online Information Review, Volume 42 (1): 13 – Feb 12, 2018

Loading next page...
 
/lp/emerald-publishing/gender-bias-in-sentiment-analysis-Mox7ryj4pq
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1468-4527
DOI
10.1108/OIR-05-2017-0139
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.Design/methodology/approachThis paper uses data sets 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.FindingsMale 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/implicationsOnly one lexical sentiment analysis algorithm was used.Practical implicationsCare 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/valueThis is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.

Journal

Online Information ReviewEmerald Publishing

Published: Feb 12, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off