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Gender bias in sentiment analysis

Mike Thelwall (School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, UK)

Online Information Review

ISSN: 1468-4527

Article publication date: 12 February 2018

Abstract

Purpose

The 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/approach

This 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.

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/implications

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.

Keywords

Citation

Thelwall, M. (2018), "Gender bias in sentiment analysis", Online Information Review, Vol. 42 No. 1, pp. 45-57. https://doi.org/10.1108/OIR-05-2017-0139

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited