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.
No
paper
NLP, representation learning, bias analysis
English
2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - 7 November 2021through 11 November 2021
9781955917094
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].
Bianchi, F; Marelli, M; Nicoli, P; Palmonari, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/396231
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