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

arXiv: https://arxiv.org/abs/2410.01987 · HTML: https://mlsanity.com/paper/2410.01987/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Learned in Translation: Contextualized Word Vectors](https://mlsanity.com/paper/1708.00107/) (2017)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Practical Text Classification With Large Pre-Trained Language Models](https://mlsanity.com/paper/1812.01207/) (2018)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [Decision support from financial disclosures with deep neural networks and transfer learning](https://mlsanity.com/paper/1710.03954/) (2017)
- [DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.13816/) (2020)
- [Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph](https://mlsanity.com/paper/2308.13534/) (2023)
- [Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning](https://mlsanity.com/paper/1901.00400/) (2018)

## Built on by (most cited first, 9 of 9)

- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [Robustness of Large Language Models Against Adversarial Attacks](https://mlsanity.com/paper/2412.17011/) (2024)
- [Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contexts](https://mlsanity.com/paper/2412.20061/) (2024)
- [Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPT](https://mlsanity.com/paper/2502.05694/) (2025)
- [MASCA: LLM based-Multi Agents System for Credit Assessment](https://mlsanity.com/paper/2507.22758/) (2025)
- [News-Aware Direct Reinforcement Trading for Financial Markets](https://mlsanity.com/paper/2510.19173/) (2025)
- [Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data](https://mlsanity.com/paper/2511.15112/) (2025)
- [Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning](https://mlsanity.com/paper/2605.30652/) (2026)
- [Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI](https://mlsanity.com/paper/2606.12210/) (2026)

## 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.