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

2026-05-10

AI Summary

This study investigates the transferability of emojis as cross-lingual, cross-platform, and cross-asset sentiment signals in financial social media. Leveraging multilingual corpora from Twitter and StockTwits in four languages, the authors systematically evaluate the role of emojis in zero-shot sentiment transfer by comparing modeling strategies based solely on text, solely on emojis, and on fused representations. The findings reveal that emojis exhibit highly stable semantics and sentiment polarity within financial contexts, functioning as a compact, language-agnostic “sentiment code.” Incorporating emojis significantly enhances model generalization across markets and effectively narrows performance gaps in cross-asset sentiment transfer, although cross-lingual transfer remains challenging.

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.

Problem

• emoji semantics
• sentiment transfer
• cross-cultural
• financial social media
• zero-shot transfer

Innovation

• emoji semantics
• cross-cultural transfer
• financial sentiment analysis
• zero-shot learning
• language-independent cues