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

被引：0
|
作者：
Liu, Chenghao [1 ]
Arulappan, Arunkumar [2 ]
Naha, Ranesh [3 ]
Mahanti, Aniket [1 ,4 ]
Kamruzzaman, Joarder [5 ]
Ra, In-Ho [6 ]
机构：
[1] Univ Auckland, Sch Comp Sci, Auckland 1010, New Zealand
[2] VIT Univ, Sch Comp Sci Engn & Informat Syst, Vellore 632014, India
[3] Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4000, Australia
[4] Univ New Brunswick, Dept Comp Sci, St John, NB E2K 5E2, Canada
[5] Federat Univ Australia, Ctr Smart Analyt, Melbourne, Vic 3806, Australia
[6] Kunsan Natl Univ, Sch Software, Gunsan 54150, South Korea
来源：
IEEE ACCESS | 2024年 / 12卷
关键词：
Sentiment analysis; Bitcoin; Analytical models; Predictive models; Large language models; Correlation; Quality assessment; Large language model; Bitcoin price; sentiment analysis; machine learning; market dynamics; INVESTOR SENTIMENT; INFORMATION; FRAMEWORK; CONTAGION; MEDIA;
D O I：
10.1109/ACCESS.2024.3445413
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：134041 / 134061
页数：21
相关论文
共 50 条
  • [21] Leveraging hierarchical language models for aspect-based sentiment analysis on financial data
    Lengkeek, Matteo
    Knaap, Finn van der
    Frasincar, Flavius
    INFORMATION PROCESSING & MANAGEMENT, 2023, 60 (05)
  • [22] AI, Machine Learning and sentiment analysis applied to financial markets and consumer markets
    Enza Messina
    Christina Erlwein-Sayer
    Gautam Mitra
    Computational Management Science, 2020, 17 : 493 - 494
  • [23] AI, Machine Learning and sentiment analysis applied to financial markets and consumer markets
    Messina, Enza
    Erlwein-Sayer, Christina
    Mitra, Gautam
    COMPUTATIONAL MANAGEMENT SCIENCE, 2020, 17 (04) : 493 - 494
  • [24] Sentiment and Emotion Analysis with Large Language Models for Political Security Prediction Framework
    Zaabar, Liyana Safra
    Yacob, Adriana Arul
    Isa, Mohd Rizal Mohd
    Wook, Muslihah
    Abdullah, Nor Asiakin
    Ramli, Suzaimah
    Razali, Noor Afiza Mat
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2025, 16 (01) : 954 - 960
  • [25] Neural network analysis and the characteristics of market sentiment in the financial markets
    McIntyre-Bhatty, YT
    EXPERT SYSTEMS, 2000, 17 (04) : 191 - 198
  • [26] Big Data Financial Sentiment Analysis in the European Bond Markets
    Pezzoli, Luca Tiozzo
    Consoli, Sergio
    Tosetti, Elisa
    MINING DATA FOR FINANCIAL APPLICATIONS, 2020, 11985 : 122 - 126
  • [27] Financial Context News Sentiment Analysis for the Lithuanian Language
    Strimaitis, Rokas
    Stefanovic, Pavel
    Ramanauskaite, Simona
    Slotkiene, Asta
    APPLIED SCIENCES-BASEL, 2021, 11 (10):
  • [28] Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains
    Fonseca, Marcio
    Cohen, Shay B.
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: ACL 2024, 2024, : 8027 - 8042
  • [29] Multifractal analysis of financial markets: a review
    Jiang, Zhi-Qiang
    Xie, Wen-Jie
    Zhou, Wei-Xing
    Sornette, Didier
    REPORTS ON PROGRESS IN PHYSICS, 2019, 82 (12)
  • [30] Large language models for newspaper sentiment analysis during COVID-19: The Guardian
    Chandra, Rohitash
    Zhu, Baicheng
    Fang, Qingying
    Shinjikashvili, Eka
    APPLIED SOFT COMPUTING, 2025, 171
12345