Gender bias in sentiment analysis

By Mike Thelwall. 2018

86 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science, Sentiment & Opinion Analysis, AI Ethics, Fairness & Society

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, neutral): “…(2015); Blodgett et al. (2016); Blodgett and O’Connor (2017); Blodgett et al. (2018); Curry and Rieser (2018); Dixon et al. (2018); Park et al. (2018); Thelwall (2018); Webster et al. (2018); Badjatiya et al. (2019); Bagdasaryan et al. (2019); Bamman et al. (2019); Bhargava and Forsyth (2019); Cao…”
Assessing Social and Intersectional Biases in Contextualized Word Representations (2019, background): “Gender bias has also been demonstrated downstream on several applications of natural language processing, including sentiment analysis [18, 30], abusive language detection [23], image captioning [16] and text classification [11].”
Theories of “Gender” in NLP Bias Research (2022, unknown): “com/openai/gpt-2-output-dataset [146] Mike Thelwall.”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “ricans names as being more angry than sentences that mention European American names, and sentences that mention women as being more emotional (more happy / more sad) than sentences that mention men. Thelwall (2018) found that automatic sentiment analysis systems are somewhat less accurate on custom”