Purpose – The examination of revisit intentions in hospitality is integral to relationship marketing and customer loyalty. Its measurement and determination have largely been done through closed-ended measures in surveys of customers. However, vast troves of consumer-generated media in the form of open-ended text reviews can also serve as sources for the determination of revisit intentions. The purpose of this paper is to develop and test a rule-based classification model from big data to extract revisit intentions. Design/methodology/approach – Data for this came from 116,241 reviews scraped from Tripadvisor. com using a stratified sampling technique comprising hotels in major cities in the USA. A sample comprising 1,800 reviews was randomly drawn from this larger pool of reviews and manually annotated. A manual-set rule-based models, supervised machine learning (ML) models and hybrid models were developed to extract revisit intention. Findings – The hybrid model of the MSRB method complemented by the gradient boosting ML method performed the best to classify revisit intentions in reviews. Practical implications – This study's rule-based classification model can be used by hotels to evaluate revisit intentions from the ever-growing pool of consumer-generated reviews. This can enable hotels to identify drivers of re-patronage and enhance relationship marketing initiatives. Originality/value – This study is the first to propose an analytical model that taps big data to extracting revisit intentions. In the past, revisit intentions have been assessed using closed-ended questions using Traditional survey-based methods