Year: 2026
Type: conference-paper
Abstract: Emojis are widely used in online financial communication, but it is unclear whether they provide transferable sentiment signals across languages, platforms, and asset communities.This study examines the extent to which emoji usage, semantics, and sentiment polarity remain stable across financial communities, and how these layers influence zero-shot sentiment transfer.Using large corpora of Twitter and Stock-Twits posts in four languages, we measure crosscommunity divergence and evaluate sentiment models trained under emoji-only, text-only, and text+emoji inputs.We find that emoji frequencies differ across communities, especially across languages, but their semantics and sentiment polarity are largely stable.Cross-asset transferability shows minimal degradation, while cross-language transfer remains the most challenging.Including emojis consistently reduces transfer gaps relative to text-only models.These results indicate that financial communication exhibits a partially shared "emoji code," and that emojis provide compact, language-independent sentiment cues that improve model generalization across markets and platforms.
Authors: Ahmed Mahrous, Roberto Di Pietro
Institution: King Abdullah University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166826222