Entity-Centric Contextual Affective Analysis

Anjalie Field, Yulia Tsvetkov


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.
Anthology ID:
P19-1243
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2550–2560
Language:
URL:
https://aclanthology.org/P19-1243
DOI:
10.18653/v1/P19-1243
Bibkey:
Cite (ACL):
Anjalie Field and Yulia Tsvetkov. 2019. Entity-Centric Contextual Affective Analysis. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2550–2560, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Entity-Centric Contextual Affective Analysis (Field & Tsvetkov, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1243.pdf