Multi-Faceted Rating of Product Reviews
Issues, Methods and Experiments
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Online product reviews are becoming increasinglyavailable, and are being used more and morefrequently by consumers in order to choose amongcompeting products. Tools that rank competingproducts in terms of the satisfaction of consumersthat have purchased the product before, are thus alsobecoming popular. We tackle the problem of rating(i.e., attributing a numerical score of satisfactionto) consumer reviews based on their textual content.We here focus on multi-facet review rating, i.e., onthe case in which the review of a product (e.g., ahotel) must be rated several times, according toseveral as...
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.
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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