# Entity-Centric Contextual Affective Analysis

## TL;DR

This paper explores how contextualized word embeddings can capture affective dimensions in social portrayals, revealing their potential and limitations for analyzing gender differences in portrayals.

## Contribution

It demonstrates the use of contextualized embeddings for affect analysis in social contexts and highlights biases affecting their applicability.

## Key findings

- Embeddings encode meaningful affect information.
- Biases limit cross-domain applicability.
- Method reveals gender portrayal differences.

## Abstract

While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.