EMNLP 2021main6 citations
SWEAT: Scoring Polarization of Topics across Different Corpora
Federico Bianchi, Marco Marelli, Paolo Nicoli, Matteo Palmonari
Abstract
Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test (SWEAT), a novel statistical measure to compute the relative polarization of a topical wordset across two distributional representations. To this end, SWEAT uses two additional wordsets, deemed to have opposite valence, to represent two different poles. We validate our approach and illustrate a case study to show the usefulness of the introduced measure.
BibTeX
@inproceedings{bianchi-etal-2021-sweat,
title = "{SWEAT}: Scoring Polarization of Topics across Different Corpora",
author = "Bianchi, Federico and
Marelli, Marco and
Nicoli, Paolo and
Palmonari, Matteo",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.788/",
doi = "10.18653/v1/2021.emnlp-main.788",
pages = "10065--10072"
}