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.

翻译：客户提供的评论已成为商家及其他消费者获取信息的重要来源。然而，有效分析海量非结构化评论仍具挑战性。尽管大语言模型（LLMs）在自然语言理解方面展现出潜力，但其在大规模评论分析中的应用受限于计算成本与可扩展性问题。本研究提出一种混合方法：利用LLMs进行方面识别，同时采用经典机器学习方法进行规模化情感分类。通过使用ChatGPT分析抽样餐厅评论，我们识别出用餐体验的关键方面，并基于人工标注评论开发情感分类器，随后将其应用于从某主流在线平台收集的历时17年、共计470万条评论。回归分析表明，我们通过机器学习标注的方面能显著解释不同用餐体验维度、菜系类型及地理区域间餐厅总体评分的方差。研究结果证明，将LLMs与传统机器学习方法相结合，可有效实现大规模客户反馈的细粒度情感分析自动化，为酒店业及其他潜在服务领域的研究者与实践者提供了一个实用框架。