This study examines the conceptual and methodological potential of predicting imminent hotel failure based on detecting early warning linguistic signals encoded and embedded in user-generated hotel guest reviews. Facilitated by modern big data mining and natural language processing (NLP) methods the study extracts and analyzes topics, sentiments, and linguistic features by comparing reviews of failed hotels versus a comparison control group. The study implemented machine learning models to detect early warning signals presaging the impending closure, bankruptcy, or failure of hotels. Guided by principles underlying linguistic signaling (Spence, 1978) and signal detection theories (Pastore & Scheirer, 1974) as well as incorporating temporal dimension (time-to-failure) in the study’s methods, findings suggest that certain linguistic cues and features (topics, sentiments, and specific “code” words) from guest reviews at certain prior periods can reliably predict looming hotel failure, giving hotel managers the opportunity to avert it.

中文翻译：

---

即将倒闭的迹象：酒店能否从客人的评论中预测破产或倒闭？


本研究探讨了基于检测编码并嵌入用户生成的酒店客人评论中的预警语言信号来预测即将发生的酒店故障的概念和方法潜力。在现代大数据挖掘和自然语言处理 (NLP) 方法的推动下，该研究通过比较失败酒店与对照组的评论来提取和分析主题、情绪和语言特征。该研究采用机器学习模型来检测预示酒店即将关闭、破产或倒闭的早期预警信号。在语言信号传导原理（Spence，1978）和信号检测理论（Pastore & Scheirer，1974）的指导下，以及在研究方法中纳入时间维度（失败时间），研究结果表明某些语言线索和特征（主题） 、情绪和特定的“暗语”）来自之前某些时期的客人评论可以可靠地预测迫在眉睫的酒店失败，使酒店管理者有机会避免它。