Beyond the Star Rating: A Scalable Framework for Aspect-Based Sentiment Analysis Using LLMs and Text Classification

TL;DR

This paper presents a scalable hybrid framework combining large language models and traditional machine learning for aspect-based sentiment analysis of millions of customer reviews, improving efficiency and explanatory power.

Contribution

It introduces a novel hybrid approach that leverages LLMs for aspect detection and classical ML for sentiment classification at scale, addressing computational challenges.

Findings

Machine-labeled aspects explain variance in restaurant ratings

The framework effectively analyzes 4.7 million reviews over 17 years

Combining LLMs with traditional ML improves large-scale sentiment analysis

Abstract

Customer-provided reviews have become an important source of information for business owners and other customers alike. However, effectively analyzing millions of unstructured reviews remains challenging. While large language models (LLMs) show promise for natural language understanding, their application to large-scale review analysis has been limited by computational costs and scalability concerns. This study proposes a hybrid approach that uses LLMs for aspect identification while employing classic machine-learning methods for sentiment classification at scale. Using ChatGPT to analyze sampled restaurant reviews, we identified key aspects of dining experiences and developed sentiment classifiers using human-labeled reviews, which we subsequently applied to 4.7 million reviews collected over 17 years from a major online platform. Regression analysis reveals that our machine-labeled aspects significantly explain variance in overall restaurant ratings across different aspects of dining experiences, cuisines, and geographical regions. Our findings demonstrate that combining LLMs with traditional machine learning approaches can effectively automate aspect-based sentiment analysis of large-scale customer feedback, suggesting a practical framework for both researchers and practitioners in the hospitality industry and potentially, other service sectors.