---
title: Quantifying Bias in Sentiment Analysis
url: https://www.ml-quant.com/papers/ssrn/4949090/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4949090
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4949090
featured: 2024-09-10
citations: unknown
topic: LLMs & Text
---


# Quantifying Bias in Sentiment Analysis

Research shows large language models used in sentiment analysis display social biases, linking specific jobs with certain genders.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4949090
- Identifier: SSRN 4949090
- Released: 2024-09-06
- First featured: Quant Letter No. 65 (2024-09-10): https://www.ml-quant.com/issues/2024-09-10/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://www.ml-quant.com/papers/ssrn/4412788/): ChatGPT predicts stock market returns using sentiment analysis, outperforming traditional methods.
- [Sentiment trading with large language models](https://www.ml-quant.com/papers/doi/10-1016-j-frl-2024-105227/): The OPT model, a large language model, has proven superior in predicting stock market returns using sentiment analysis of U.S. financial news, outdoing traditional methods like the Loughran-McDonald dictionary model.
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://www.ml-quant.com/papers/arxiv/2401.05799/): A study suggests using large language models without fine-tuning for financial sentiment analysis, offering a design framework that enhances accuracy.
- [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://www.ml-quant.com/papers/arxiv/2306.12659/): A new approach improves financial sentiment analysis by addressing limitations of language models.
- [SYNTHEVAL: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists](https://www.ml-quant.com/papers/arxiv/2408.17437/): SYNTHEVAL is a testing framework that uses large language models to generate tests for evaluating NLP models, particularly in sentiment analysis and toxic language detection.
- [The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models](https://www.ml-quant.com/papers/arxiv/2406.19358/): The study finds Small Multilingual Language Models (SMLM) excel in zero-shot cross-lingual sentiment analysis, while Large Language Models (LLM) perform better in few-shot scenarios.