Leveraging Large Language Models for Sentiment Analysis in E-Commerce Product Reviews

Sentiment analysis has gained significant attention due to its ability to extract insights from unstructured data, such as reviews, social media posts, and customer feedback. Large language models (LLMs), with their advanced natural language understanding capabilities, have revolutionized sentiment analysis by providing more accurate and nuanced results. This paper explores the application of LLMs in sentiment analysis within the e-commerce domain, where customer feedback is abundant and crucial for business decisions. We demonstrate the effectiveness of LLMs in analyzing sentiments in product reviews, comparing their performance with traditional machine learning models. Experimental results show that LLMs significantly outperform traditional models in terms of accuracy and contextual understanding. The study also discusses the potential challenges and limitations of LLMs in sentiment analysis, as well as future research directions in this area.