FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media

2026-05-10

AI Summary

This study investigates whether emojis in financial social media can serve as effective proxies for investor sentiment to predict market trends. Leveraging a balanced dataset of approximately 528,000 emoji-containing posts from StockTwits, the authors systematically evaluate—for the first time—the feasibility of conducting financial sentiment analysis using emojis alone, employing both logistic regression and Transformer-based models. Experimental results demonstrate that an emoji-only model achieves an F1 score of 0.75, which improves to 0.88 when combined with textual content; notably, certain emoji combinations yield prediction accuracies exceeding 90%. The findings reveal that emoji usage in financial contexts exhibits strong domain-specific patterns and highlight their low computational overhead, underscoring their potential utility in latency-sensitive applications such as high-frequency trading.

Abstract

This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent in digital communication, have potential as compact indicators of investor sentiment, which can be critical for predicting market trends. Our study examines whether emojis alone can serve as reliable proxies for financial sentiment and how they compare with traditional text-based analysis. We conduct a series of experiments using logistic regression and transformer models. We further analyze the performance, computational efficiency, and data requirements of emoji-based versus text-based sentiment classification. Using a balanced dataset of about 528,000 emoji-containing StockTwits posts, we find that emoji-only models achieve F1 approximately 0.75, lower than text-emoji combined models, which achieve F1 approximately 0.88, but with far lower computational cost. This is a useful feature in time-sensitive settings such as high-frequency trading. Furthermore, certain emojis and emoji pairs exhibit strong predictive power for market sentiment, demonstrating over 90 percent accuracy in predicting bullish or bearish trends. Finally, our research reveals large statistical differences in emoji usage between financial and general social media contexts, stressing the need for domain-specific sentiment analysis models.

Problem

• emoji
• sentiment analysis
• financial social media
• market sentiment
• StockTwits

Innovation

• emoji-driven sentiment analysis
• financial social media
• computational efficiency
• domain-specific modeling
• market trend prediction