Emojinize: Enriching Any Text with Emoji Translations
Lars Henning Klein, Roland Aydin, Robert West

TL;DR
Emojinize is a novel method that uses large language models to automatically translate any text into contextually appropriate emoji sequences, enhancing expressiveness and enabling new applications.
Contribution
It introduces Emojinize, the first automated approach for translating arbitrary text into emoji, leveraging large language models for disambiguation and compositional expression.
Findings
Emoji translations increase guessability of masked words by 55%.
Human-selected emoji improve guessability by 29%.
Emoji can accurately represent complex concepts.
Abstract
Emoji have become ubiquitous in written communication, on the Web and beyond. They can emphasize or clarify emotions, add details to conversations, or simply serve decorative purposes. This casual use, however, barely scratches the surface of the expressive power of emoji. To further unleash this power, we present Emojinize, a method for translating arbitrary text phrases into sequences of one or more emoji without requiring human input. By leveraging the power of large language models, Emojinize can choose appropriate emoji by disambiguating based on context (eg, cricket-bat vs bat) and can express complex concepts compositionally by combining multiple emoji (eq, "Emojinize" is translated to input-latin-letters right-arrow grinning-face). In a cloze test--based user study, we show that Emojinize's emoji translations increase the human guessability of masked words by 55%, whereas…
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Taxonomy
TopicsDigital Communication and Language · Linguistics, Language Diversity, and Identity · Second Language Acquisition and Learning
