Comparing LLMs for Sentiment Analysis in Financial Market News
This work addresses sentiment analysis for financial applications, but it is incremental as it applies existing methods to a specific domain.
The study compared large language models (LLMs) with classical approaches for sentiment analysis of financial market news, finding that LLMs outperformed classical models in most cases.
This article presents a comparative study of large language models (LLMs) in the task of sentiment analysis of financial market news. This work aims to analyze the performance difference of these models in this important natural language processing task within the context of finance. LLM models are compared with classical approaches, allowing for the quantification of the benefits of each tested model or approach. Results show that large language models outperform classical models in the vast majority of cases.