Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that also has been labeled with consumers' self-reported ratings of 16 consumption-related emotions and four evaluation constructs: trust, commitment, recommendation, and sentiment. LX consistently outperforms leading models, including GPT-4 Turbo, RoBERTa, and DeepSeek, achieving 81% macro-F1 accuracy on open-ended survey responses and greater than 95% accuracy on third-party-annotated Amazon and Yelp reviews. An application of LX to online retail data, using seemingly unrelated regression, affirms that review-expressed emotions predict product ratings, which in turn predict purchase behavior. Most emotional effects are mediated by product ratings, though some emotions, such as discontent and peacefulness, influence purchase directly, indicating that emotional tone provides meaningful signals beyond star ratings. To support its use, a no-code, cost-free, LX web application is available, enabling scalable analyses of consumer-authored text. In establishing a new methodological foundation for consumer perception measurement, this research demonstrates new methods for leveraging large language models to advance marketing research and practice, thereby achieving validated detection of marketing constructs from consumer data.

翻译：准确测量非结构化文本中的消费者情感与评价始终是营销研究与实务的核心挑战。本研究提出语言提取器（LX），这是一个基于消费者撰写文本进行微调的大语言模型，这些文本已标注消费者自我报告的16种消费相关情感及四个评价构念：信任、承诺、推荐与情感倾向。LX在多项任务中持续超越包括GPT-4 Turbo、RoBERTa和DeepSeek在内的领先模型，在开放式调查回复中达到81%的宏观F1准确率，在第三方标注的亚马逊和Yelp评论中准确率超过95%。通过将LX应用于在线零售数据并采用似不相关回归分析，证实评论表达的情感能预测产品评分，进而预测购买行为。大多数情感效应通过产品评分中介传导，但部分情感（如不满与平静）会直接影响购买，表明情感基调能提供超越星级评分的有效信号。为支持实际应用，本研究提供了无需编码、免费使用的LX网络应用程序，可实现消费者撰写文本的可扩展分析。通过为消费者感知测量建立新的方法论基础，本研究展示了利用大语言模型推进营销研究与实务的创新方法，从而实现了从消费者数据中有效检测营销构念的验证路径。