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.
Bianchi, F., Marelli, M., Nicoli, P., Palmonari, M. (2021). SWEAT: Scoring Polarization of Topics across Different Corpora. In EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp.10065-10072). Association for Computational Linguistics (ACL) [10.18653/v1/2021.emnlp-main.788].
SWEAT: Scoring Polarization of Topics across Different Corpora
Bianchi, Federico;Marelli, Marco;Palmonari, Matteo
2021
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.
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