Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT
Financial sentiment analysis (FSA) is crucial for evaluating market sentiment and making well-informed financial decisions. The advent of large language models (LLMs) such as BERT and its financial variant, FinBERT, has notably enhanced sentiment analysis capabilities. This paper investigates the ap...
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Published in | IEEE International Conference on Power, Intelligent Computing and Systems (Online) pp. 717 - 721 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
26.07.2024
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Subjects | |
Online Access | Get full text |
ISSN | 2834-8567 |
DOI | 10.1109/ICPICS62053.2024.10796670 |
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Abstract | Financial sentiment analysis (FSA) is crucial for evaluating market sentiment and making well-informed financial decisions. The advent of large language models (LLMs) such as BERT and its financial variant, FinBERT, has notably enhanced sentiment analysis capabilities. This paper investigates the application of LLMs and FinBERT for FSA, comparing their performance on news articles, financial reports and company announcements. The study emphasizes the advantages of prompt engineering with zero-shot and few-shot strategy to improve sentiment classification accuracy. Experimental results indicate that GPT -40, with few-shot examples of financial texts, can be as competent as a well fine-tuned FinBERT in this specialized field. |
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AbstractList | Financial sentiment analysis (FSA) is crucial for evaluating market sentiment and making well-informed financial decisions. The advent of large language models (LLMs) such as BERT and its financial variant, FinBERT, has notably enhanced sentiment analysis capabilities. This paper investigates the application of LLMs and FinBERT for FSA, comparing their performance on news articles, financial reports and company announcements. The study emphasizes the advantages of prompt engineering with zero-shot and few-shot strategy to improve sentiment classification accuracy. Experimental results indicate that GPT -40, with few-shot examples of financial texts, can be as competent as a well fine-tuned FinBERT in this specialized field. |
Author | Shen, Yanxin Zhang, Pulin Kirin |
Author_xml | – sequence: 1 givenname: Yanxin surname: Shen fullname: Shen, Yanxin email: ssyysyx@zju.edu.cn organization: Zhejiang University,Hangzhou,Zhejiang,China – sequence: 2 givenname: Pulin Kirin surname: Zhang fullname: Zhang, Pulin Kirin email: kirinpzhang@gmail.com organization: Lehigh University,Bethlehem,Pennsylvania,USA |
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Snippet | Financial sentiment analysis (FSA) is crucial for evaluating market sentiment and making well-informed financial decisions. The advent of large language models... |
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SubjectTerms | Accuracy Analytical models BERT Computational modeling Context modeling FinBERT Fluctuations Large language models NLP Prompt engineering Real-time systems Sentiment analysis Zero shot learning |
Title | Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT |
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