mlsanityData to 7 Oct 2026

Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT

arXiv 2410.01987 ↗First posted 2 Oct 2024cs.IR cs.CL cs.SIcited by 9

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Abstract (arXiv)

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-4o, with few-shot examples of financial texts, can be as competent as a well fine-tuned FinBERT in this specialized field.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

22 of 35 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  5. 2023A Survey of Large Language Modelscited by 2,537
  6. 2018Deep contextualized word representationscited by 2,285
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  8. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 599
  9. 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 414
  10. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
  11. 2021Towards Emotional Support Dialog Systemscited by 294
  12. 2018Deep Learning: A Critical Appraisalcited by 214
10 more
  1. 2017Regularizing and Optimizing LSTM Language Modelscited by 205
  2. 2017Learned in Translation: Contextualized Word Vectorscited by 104
  3. 2020Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Researchcited by 67
  4. 2023A Bibliometric Review of Large Language Models Research from 2017 to 2023cited by 56
  5. 2018Practical Text Classification With Large Pre-Trained Language Modelscited by 24
  6. 2016The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Paperscited by 20
  7. 2017Decision support from financial disclosures with deep neural networks and transfer learningcited by 18
  8. 2020DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysiscited by 9
  9. 2023Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graphcited by 5
  10. 2018Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learningcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2024Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layoutcited by 14
  2. 2024Robustness of Large Language Models Against Adversarial Attackscited by 12
  3. 2024Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contextscited by 7
  4. 2025Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPTcited by 5
  5. 2025MASCA: LLM based-Multi Agents System for Credit Assessmentcited by 3
  6. 2025News-Aware Direct Reinforcement Trading for Financial Marketscited by 0
  7. 2025Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Datacited by 0
  8. 2026Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learningcited by 0
  9. 2026Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AIcited by 0