2021
EMNLP
EMNLP 2021
SWEAT: Scoring Polarization of Topics across Different Corpora
Abstract
AbstractUnderstanding 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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Interdisciplinary Bridge
— Artificial Intelligence and Data Science & Analytics and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— topic polarization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Metric Learning
Machine Learning > Core Methods > Embedding Learning
Machine Learning > Optimization & Theory > Statistical Learning
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Learning Types > Representation Learning
Natural Language Processing > Applications > Sentiment Analysis
Artificial Intelligence > Core AI > Natural Language Processing
Data Science & Analytics > Applications > Social Media Analysis