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Understanding differences of viewpoints across corpora is a fundamental taskfor computational social sciences. In this paper, we propose the Sliced WordEmbedding Association Test (SWEAT), a novel statistical measure to compute therelative polarization of a topical wordset across two distributionalrepresentations. To this end, SWEAT uses two additional wordsets, deemed tohave opposite valence, to represent two different poles. We validate ourapproach and illustrate a case study to show the usefulness of the introducedmeasure.

SWEAT: Scoring Polarization of Topics across Different Corpora