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        <title>Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis</title>
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        <namePart type="given">Jayadev</namePart>
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            <title>Proceedings of the First Workshop on Gender Bias in Natural Language Processing</title>
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            <namePart type="given">Marta</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Costa-jussà</namePart>
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        <name type="personal">
            <namePart type="given">Christian</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Will</namePart>
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    <abstract>In this work, we investigate the presence of occupational gender stereotypes in sentiment analysis models. Such a task has implications in reducing implicit biases in these models, which are being applied to an increasingly wide variety of downstream tasks. We release a new gender-balanced dataset of 800 sentences pertaining to specific professions and propose a methodology for using it as a test bench to evaluate sentiment analysis models. We evaluate the presence of occupational gender stereotypes in 3 different models using our approach, and explore their relationship with societal perceptions of occupations.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3809</identifier>
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%0 Conference Proceedings
%T Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis
%A Bhaskaran, Jayadev
%A Bhallamudi, Isha
%Y Costa-jussà, Marta R.
%Y Hardmeier, Christian
%Y Radford, Will
%Y Webster, Kellie
%S Proceedings of the First Workshop on Gender Bias in Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F bhaskaran-bhallamudi-2019-good
%X In this work, we investigate the presence of occupational gender stereotypes in sentiment analysis models. Such a task has implications in reducing implicit biases in these models, which are being applied to an increasingly wide variety of downstream tasks. We release a new gender-balanced dataset of 800 sentences pertaining to specific professions and propose a methodology for using it as a test bench to evaluate sentiment analysis models. We evaluate the presence of occupational gender stereotypes in 3 different models using our approach, and explore their relationship with societal perceptions of occupations.
%R 10.18653/v1/W19-3809
%U https://aclanthology.org/W19-3809
%U https://doi.org/10.18653/v1/W19-3809
%P 62-68

Markdown (Informal)

[Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis](https://aclanthology.org/W19-3809) (Bhaskaran & Bhallamudi, GeBNLP 2019)

• Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis (Bhaskaran & Bhallamudi, GeBNLP 2019)

ACL

• Jayadev Bhaskaran and Isha Bhallamudi. 2019. Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, pages 62–68, Florence, Italy. Association for Computational Linguistics.