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

arXiv: https://arxiv.org/abs/1906.10256 · HTML: https://mlsanity.com/paper/1906.10256/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 19 of 19)

- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://mlsanity.com/paper/2106.14574/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text](https://mlsanity.com/paper/2310.17428/) (2023)
- [CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias](https://mlsanity.com/paper/2308.12539/) (2023)
- [The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis](https://mlsanity.com/paper/2310.12318/) (2023)
- [In the Name of Fairness: Assessing the Bias in Clinical Record De-identification](https://mlsanity.com/paper/2305.11348/) (2023)
- [Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias](https://mlsanity.com/paper/2204.10365/) (2022)
- [Measuring Harmful Representations in Scandinavian Language Models](https://mlsanity.com/paper/2211.11678/) (2022)
- [Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs](https://mlsanity.com/paper/2505.02456/) (2025)
- [Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways](https://mlsanity.com/paper/2402.04420/) (2024)
- [Measuring Normative and Descriptive Biases in Language Models Using Census Data](https://mlsanity.com/paper/2304.05764/) (2023)
- [Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality](https://mlsanity.com/paper/2408.02948/) (2024)
- [Measuring Gender Bias in Job Title Matching for Grammatical Gender Languages](https://mlsanity.com/paper/2509.13803/) (2025)
- [Blacks is to Anger as Whites is to Joy? Understanding Latent Affective Bias in Large Pre-trained Neural Language Models](https://mlsanity.com/paper/2301.09003/) (2023)
- [Learning Horn Envelopes via Queries from Large Language Models](https://mlsanity.com/paper/2305.12143/) (2023)
- [Exploring Gender Bias Beyond Occupational Titles](https://mlsanity.com/paper/2507.02679/) (2025)

## Abstract (arXiv)

In this work, we investigate the presence of occupational gender stereotypes in sentiment analysis models. Such a task has implications for 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.