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

TL;DR

This study investigates the effectiveness of emojis as standalone indicators for financial sentiment analysis on StockTwits, comparing their performance with traditional text-based methods and highlighting their efficiency in time-sensitive trading scenarios.

Contribution

It introduces a comprehensive analysis of emoji-only sentiment models in finance, demonstrating their predictive power and efficiency relative to text-based approaches.

Findings

Emoji-only models achieve about 0.75 F1 score, lower than combined models at 0.88.

Certain emojis predict market trends with over 90% accuracy.

Emoji usage significantly differs between financial and general social media contexts.

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.