# Transforming Sentiment Analysis in the Financial Domain with ChatGPT

arXiv: https://arxiv.org/abs/2308.07935 · HTML: https://mlsanity.com/paper/2308.07935/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 13 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)

## Built on by (most cited first, 25 of 25)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks](https://mlsanity.com/paper/2407.12994/) (2024)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing](https://mlsanity.com/paper/2505.16491/) (2025)
- [A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification](https://mlsanity.com/paper/2411.02476/) (2024)
- [VirtualXAI: A User-Centric Framework for Explainability Assessment Leveraging GPT-Generated Personas](https://mlsanity.com/paper/2503.04261/) (2025)
- [Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents](https://mlsanity.com/paper/2504.05527/) (2025)
- [Monetizing Currency Pair Sentiments through LLM Explainability](https://mlsanity.com/paper/2407.19922/) (2024)
- [Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach](https://mlsanity.com/paper/2412.06837/) (2024)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [Automated Coding of Communications in Collaborative Problem-solving Tasks Using ChatGPT](https://mlsanity.com/paper/2411.10246/) (2024)
- [Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features](https://mlsanity.com/paper/2508.15825/) (2025)
- [HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning](https://mlsanity.com/paper/2509.04303/) (2025)
- [Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis](https://mlsanity.com/paper/2509.12611/) (2025)
- [Evaluating Large Language Models for Stance Detection on Financial Targets from SEC Filing Reports and Earnings Call Transcripts](https://mlsanity.com/paper/2510.23464/) (2025)
- [Bridging the Arithmetic Gap: The Cognitive Complexity Benchmark and Financial-PoT for Robust Financial Reasoning](https://mlsanity.com/paper/2601.21157/) (2026)
- [Signal or Noise in Multi-Agent LLM-based Stock Recommendations?](https://mlsanity.com/paper/2604.17327/) (2026)
- [DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behaviour](https://mlsanity.com/paper/2407.13408/) (2024)
- [Impact of ChatGPT on the writing style of condensed matter physicists](https://mlsanity.com/paper/2408.17325/) (2024)
- [Large Language Models and Futures Price Factors in China](https://mlsanity.com/paper/2509.23609/) (2025)
- [Automated Coding of Communication Data Using ChatGPT: Consistency Across Subgroups](https://mlsanity.com/paper/2510.20584/) (2025)
- [Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AI](https://mlsanity.com/paper/2605.11687/) (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)
- [From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models](https://mlsanity.com/paper/2608.06020/) (2026)

## Abstract (arXiv)

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35% enhanced performance in sentiment classification and a 36% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.