用大模型分析加密币讨论中的预测情绪，发现Matic最乐观。
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models
- 用GPT-4o分类评论为预测性/非预测性，细分乐观、悲观等类型。
- Matic用户更倾向发布乐观预测，其他币种情绪模式各异。
- 适合关注加密市场情绪与投资者行为的研究者参考。
本研究通过先进自然语言处理技术，分析加密货币讨论中预测性言论、希望表达与后悔检测行为。提出新型分类体系“预测性陈述”，将评论分为预测性上升、下降、中立及非预测类。基于GPT-4o模型，对Cardano、Binance、Matic、Fantom和Ripple五种主流加密货币展开分析，揭示其预测情绪的显著差异，其中Matic表现出更高的乐观预测倾向。同时考察希望与后悔情绪，发现二者与预测行为存在复杂互动关系。尽管受限于数据量与资源，研究仍揭示了投资者行为与情绪趋势的重要发现，可为战略决策与未来研究提供支持。
原文摘要 · Abstract (English)
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.
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