Large Language Models and Sentiment Analysis in Financial Markets: A Review, Datasets, and Case Study

【Author】 Liu, Chenghao; Arulappan, Arunkumar; Naha, Ranesh; Mahanti, Aniket; Kamruzzaman, Joarder; Ra, In-Ho

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】This paper comprehensively examines Large Language Models (LLMs) in sentiment analysis, specifically focusing on financial markets and exploring the correlation between news sentiment and Bitcoin prices. We systematically categorize various LLMs used in financial sentiment analysis, highlighting their unique applications and features. We also investigate the methodologies for effective data collection and categorization, underscoring the need for diverse and comprehensive datasets. Our research features a case study investigating the correlation between news sentiment and Bitcoin prices, utilizing advanced sentiment analysis and financial analysis methods to demonstrate the practical application of LLMs. The findings reveal a modest but discernible correlation between news sentiment and Bitcoin price fluctuations, with historical news patterns showing a more substantial impact on Bitcoin's longer-term price than immediate news events. This highlights LLMs' potential in market trend prediction and informed investment decision-making.

【Keywords】Sentiment analysis; Bitcoin; Analytical models; Predictive models; Large language models; Correlation; Quality assessment; Large language model; Bitcoin price; sentiment analysis; machine learning; market dynamics

【发表时间】2024

【收录时间】2024-10-13

【文献类型】综述

【主题类别】

区块链治理-市场治理-情感分析