Cross-Cultural Transfer of Emoji Semantics and Sentiment in Financial Social Media

TL;DR

This paper investigates how emojis in financial social media maintain stable sentiment signals across languages and platforms, enhancing cross-community sentiment transfer despite some divergence.

Contribution

It demonstrates that emojis serve as language-independent sentiment cues, improving zero-shot transferability of sentiment models across financial communities.

Findings

Emoji semantics and sentiment polarity are largely stable across communities.

Including emojis reduces transfer gaps in sentiment models.

Cross-language transfer remains the most challenging aspect.

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 StockTwits posts in four languages, we measure cross-community 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.