Online product reviews are becoming increasingly available, 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 textual content. We here focus on multi-facet review rating, i.e., on the case in which the review of a product (e.g., a hotel) must be rated several times, according to several aspects of the product (for a hotel: cleanliness, centrality of location, etc.). We explore several aspects of the problem, with special emphasis on how to generate vectorial representations of the text by means of POS tagging, sentiment analysis, and feature selection for ordinal regression learning. We present the results of experiments conducted on a dataset of more than 15,000 reviews that we have crawled from a popular hotel review site.

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