Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models

TL;DR

This paper analyzes sentiment and predictive behaviors in cryptocurrency discussions using GPT-4o, revealing distinct sentiment patterns and emotional interplay across five cryptocurrencies, with implications for investor behavior and future research.

Contribution

It introduces a novel classification scheme for predictive statements and applies large language models to analyze sentiment dynamics in cryptocurrency discussions.

Findings

Matic shows higher optimistic prediction propensity

Distinct sentiment patterns across cryptocurrencies

Nuanced interplay between hope and regret sentiments

Abstract

This study performs analysis of Predictive statements, Hope speech, and Regret Detection behaviors within cryptocurrency-related discussions, leveraging advanced natural language processing techniques. We introduce a novel classification scheme named "Prediction statements," categorizing comments into Predictive Incremental, Predictive Decremental, Predictive Neutral, or Non-Predictive categories. Employing GPT-4o, a cutting-edge large language model, we explore sentiment dynamics across five prominent cryptocurrencies: Cardano, Binance, Matic, Fantom, and Ripple. Our analysis reveals distinct patterns in predictive sentiments, with Matic demonstrating a notably higher propensity for optimistic predictions. Additionally, we investigate hope and regret sentiments, uncovering nuanced interplay between these emotions and predictive behaviors. Despite encountering limitations related to data volume and resource availability, our study reports valuable discoveries concerning investor behavior and sentiment trends within the cryptocurrency market, informing strategic decision-making and future research endeavors.