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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.
About the author
Stefano Baccianella is actually a Researcher at the Institute ofInformation Science and Technologies - National Council ofReasearch, Pisa, Italy.His research topics concerns all the aspects of theclassification of multimedia documents. In particular theclassification of textual documents and the analysis of thesentiment carried by them.
