• reviews
• regression
• pattern
• tripadvisor
• rating
• hotel
• jatecs
• classifier

Online product reviews are becoming increasingly popular, and are being
used more and more frequently by consumers in order to choose among
competing products. Tools that rank competing products in terms of the
satisfaction of consumers that have purchased the product before, are thus
also becoming popular. We tackle the problem of rating (i.e., attributing
a numerical score of satisfaction to) consumer reviews based on their tex-
tual content. In this work we focus on multi-facet rating of hotel reviews,
i.e., on the case in which the review of a hotel must be rated several times,
according to several aspects (e.g., cleanliness, dining facilities, centrality of
location). We explore several aspects of the problem, including the vectorial
representation of the text based on sentiment analysis, collocation analysis,
and feature selection for ordinal-regression learning. We present the results
of experiments conducted on a corpus of approximately 15,000 hotel reviews
that we have crawled from a popular hotel review site.