Deciphering Political Entity Sentiment in News with Large Language
  Models: Zero-Shot and Few-Shot Strategies
Alapan Kuila Sudeshna Sarkar
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 few-shot in-context learning, we assess whether
this method enhances sentiment prediction accuracy. Our evaluation on
sentiment-labeled datasets demonstrates that LLMs, outperform fine-tuned BERT
models in capturing entity-specific sentiment. We find that learning in-context
significantly improves model performance, while the self-consistency mechanism
enhances consistency in sentiment prediction. Despite the promising results, we
observe inconsistencies in the effectiveness of the COT prompting method.
Overall, our findings underscore the potential of LLMs in entity-centric
sentiment analysis within the political news domain and highlight the
importance of suitable prompting strategies and model architectures.