SWEAT: Scoring Polarization of Topics across Different Corpora
Federico Bianchi | Marco Marelli | Paolo Nicoli | Matteo Palmonari
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
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.