Leveraging Large Language Models for Aspect-Based Sentiment Classification using GPT-4

Abstract

Listen

Translate article

Aspect-based sentiment analysis (ABSA) is an essential task in natural language processing (NLP) that aims to extract both aspects and their corresponding sentiment polarities from textual data. Despite its importance, ABSA faces challenges in accurately capturing nuanced sentiments, particularly in complex and context-dependent scenarios. This paper introduces a novel technique that employs few-shot learning with the GPT-4 model to determine the sentiment of specific aspects within the text. By using a template prompt along with few-shot examples, we enhance ChatGPT’s in-context learning capability, thereby improving the effectiveness of aspect-based sentiment classification. Our tests and evaluations demonstrate that this method achieves state-of-the-art results in ABSA. This research represents a significant advancement in sentiment analysis methodologies, especially in capturing nuanced sentiments toward specific aspects.