This research proposes a systematic, large language model (LLM) approach for extracting product and service attributes, features, and associated sentiments from customer reviews. Grounded in marketing theory, the framework distinguishes perceptual attributes from actionable features, producing interpretable and managerially actionable insights. We apply the methodology to 20,000 Yelp reviews of Starbucks stores and evaluate eight prompt variants on a random subset of reviews. Model performance is assessed through agreement with human annotations and predictive validity for customer ratings. Results show high consistency between LLMs and human coders and strong predictive validity, confirming the reliability of the approach. Human coders required a median of six minutes per review, whereas the LLM processed each in two seconds, delivering comparable insights at a scale unattainable through manual coding. Managerially, the analysis identifies attributes and features that most strongly influence customer satisfaction and their associated sentiments, enabling firms to pinpoint "joy points," address "pain points," and design targeted interventions. We demonstrate how structured review data can power an actionable marketing dashboard that tracks sentiment over time and across stores, benchmarks performance, and highlights high-leverage features for improvement. Simulations indicate that enhancing sentiment for key service features could yield 1-2% average revenue gains per store.

翻译：本研究提出了一种系统性的大语言模型（LLM）方法，用于从客户评论中提取产品与服务的属性、特征及相关情感。该框架以营销理论为基础，区分感知属性与可操作特征，从而产生可解释且具有管理可操作性的洞见。我们将该方法应用于20,000条Yelp平台上星巴克门店的评论，并在随机抽取的评论子集上评估了八种提示变体。模型性能通过与人工标注的一致性以及对客户评分的预测效度进行评估。结果显示，LLM与人工编码者之间具有高度一致性，且预测效度强，证实了该方法的可靠性。人工编码者处理每条评论的中位时间为六分钟，而LLM仅需两秒，便能提供与人工编码相当但规模远超手动处理的洞见。在管理层面，该分析识别出对客户满意度影响最强的属性与特征及其相关情感，使企业能够准确定位“愉悦点”、解决“痛点”并设计有针对性的干预措施。我们展示了结构化的评论数据如何驱动一个可操作的营销仪表板，该仪表板可追踪跨时间和跨门店的情感趋势、进行绩效对标，并突出高杠杆特征以指导改进。模拟分析表明，提升关键服务特征的情感倾向可为每家门店带来1-2%的平均收入增长。