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.
@article{arxiv.2109.07231,
title = {SWEAT: Scoring Polarization of Topics across Different Corpora},
author = {Federico Bianchi and Marco Marelli and Paolo Nicoli and Matteo Palmonari},
journal= {arXiv preprint arXiv:2109.07231},
year = {2021}
}