emoji在金融社交中传递情感信号，跨语言仍具稳定性。
Cross-Cultural Transfer of Emoji Semantics and Sentiment in Financial Social Media
- 分析多语言金融社交媒体中emoji的使用与情感一致性
- 跨资产转移误差小，跨语言转移仍具挑战
- 加入emoji可显著提升模型跨平台泛化能力
表情符号广泛用于在线金融交流，但其情感信号是否在不同语言、平台和资产社区间可迁移尚不明确。本研究基于四种语言的Twitter和StockTwits大规模语料，考察了表情符号使用、语义及情感极性在金融社群中的稳定性，并评估了仅用表情符号、仅用文本、以及文本+表情符号输入下训练的情感模型的零样本迁移性能。结果表明：表情符号使用频率在不同社群间存在差异，尤其在语言层面差异显著，但其语义和情感极性总体保持稳定。跨资产迁移几乎无性能下降，而跨语言迁移仍是最具挑战性的环节。使用表情符号能持续缩小迁移差距，相比纯文本模型表现更优。研究发现金融交流存在部分共享的“表情符号代码”，表情符号提供了紧凑、语言无关的情感线索，有助于提升模型在不同市场与平台间的泛化能力。
原文摘要 · Abstract (English)
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.
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