Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models
<|reference_start|>Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models: 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.<|reference_end|>
@article{tash2024exploring,
    title={Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency
  Discussions by Few-Shot Learning with Large Language Models},
    author={Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Olga Kolesnikova, and
  Grigori Sidorov},
    journal={arXiv preprint arXiv:2409.02836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02836},
    primaryClass={cs.CL cs.AI cs.CE cs.LG}
}