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

## TL;DR

This paper examines occupational gender stereotypes in sentiment analysis models, introducing a gender-balanced dataset and a methodology to evaluate biases, revealing societal influences on model perceptions of professions.

## Contribution

It presents a new gender-balanced dataset and a test bench methodology to assess occupational stereotypes in sentiment analysis models.

## Key findings

- Identified biases in three sentiment analysis models
- Linked model stereotypes to societal perceptions
- Provided a dataset for bias evaluation in NLP models

## Abstract

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.