Abstract
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.
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