Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis

Jayadev Bhaskaran, Isha Bhallamudi


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.
Anthology ID:
W19-3809
Volume:
Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Marta R. Costa-jussà, Christian Hardmeier, Will Radford, Kellie Webster
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
62–68
Language:
URL:
https://aclanthology.org/W19-3809
DOI:
10.18653/v1/W19-3809
Bibkey:
Cite (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.
Cite (Informal):
Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis (Bhaskaran & Bhallamudi, GeBNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3809.pdf
Code
 jayadevbhaskaran/gendered-sentiment
Data
SSTSST-2