LLMs and NLP Models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study

Author:

Roumeliotis Konstantinos I.1ORCID,Tselikas Nikolaos D.1ORCID,Nasiopoulos Dimitrios K.2ORCID

Affiliation:

1. Department of Informatics and Telecommunications, University of Peloponnese, Akadimaikou G. K. Vlachou Street, 22131 Tripoli, Greece

2. Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 11855 Athens, Greece

Abstract

Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets.

Publisher

MDPI AG

Reference61 articles.

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