Emojinize: Enriching Any Text with Emoji Translations

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 human-picked emoji translations do so by only 29%. These results suggest that emoji provide a sufficiently rich vocabulary to accurately translate a wide variety of words. Moreover, annotating words and phrases with Emojinize's emoji translations opens the door to numerous downstream applications, including children learning how to read, adults learning foreign languages, and text understanding for people with learning disabilities.