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Crowdsourcing has become an essential source of information for tourism stakeholders. Every day, tourists leave large volumes of feedback data in the form of posts, likes, textual reviews, and ratings in dedicated crowdsourcing platforms. This behavior makes the analysis of crowdsourced information strategic, allowing the discovery of important knowledge regarding tourists and tourism resources. This paper presents a survey on the analysis and prediction of hotel ratings from crowdsourced data, covering both off‐line (batch) and on‐line (stream‐based) processing. Specifically, it reports multiple rating‐based profiling, recommendation, and evaluation techniques. While most of the surveyed works adopt entity‐based multicriteria profiling, prerecommendation filtering, and off‐line processing, the latest hotel rating prediction trends include feature‐based, trust and reputation modeling, postrecommendation filtering …