Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies

TL;DR

This paper investigates how prompting strategies, including RolePlaying, Chain-of-thought, and their combination, can significantly improve large language models' accuracy in multi-domain sentiment analysis tasks.

Contribution

It introduces two novel prompting strategies and a combined approach specifically designed for sentiment analysis, demonstrating their effectiveness across multiple domains.

Findings

Prompting strategies improve sentiment analysis accuracy.

CoT prompting enhances implicit sentiment detection.

RP-CoT outperforms individual strategies.

Abstract

Large Language Models (LLMs) have made significant strides in both scientific research and practical applications. Existing studies have demonstrated the state-of-the-art (SOTA) performance of LLMs in various natural language processing tasks. However, the question of how to further enhance LLMs' performance in specific task using prompting strategies remains a pivotal concern. This paper explores the enhancement of LLMs' performance in sentiment analysis through the application of prompting strategies. We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis: RolePlaying (RP) prompting and Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT prompting strategy which is a combination of RP prompting and CoT prompting. We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies. The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy. Further, the CoT prompting strategy exhibits a notable impact on implicit sentiment analysis, with the RP-CoT prompting strategy delivering the most superior performance among all strategies.