Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies

arXiv:2404.04361v1 Announce Type: new
Abstract: Sentiment analysis plays a pivotal role in understanding public opinion, particularly in the political domain where the portrayal of entities in news articles influences public perception. In this paper, we investigate the effectiveness of Large Language Models (LLMs) in predicting entity-specific sentiment from political news articles. Leveraging zero-shot and few-shot strategies, we explore the capability of LLMs to discern sentiment towards political entities in news content. Employing a chain-of-thought (COT) approach augmented with rationale in …

abstract analysis articles arxiv cs.cl domain few-shot language language models large language large language models llms opinion paper perception pivotal political public role sentiment sentiment analysis strategies type understanding zero-shot