2022
EMNLP
EMNLP 2022
How to Do Things without Words: Modeling Semantic Drift of Emoji
Abstract
AbstractEmoji have become a significant part of our informal textual communication. Previous work, addressing the societal and linguistic functions of emoji, overlooked the relation between the semantics and the visual variations of the symbols. In this paper we model and analyze the semantic drift of emoji and discuss the features that may be contributing to the drift, some are unique to emoji and some are more general. Specifically, we explore the relations between graphical changes and semantic changes.
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Interdisciplinary Bridge
— Data Science & Analytics and Interdisciplinary and Natural Language Processing
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Keyword Pioneer
— visual variation
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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, Speech & Audio