Istituto di Scienza e Tecnologie dell'Informazione     
Baccianella S., Esuli A., Sebastiani F. Multi-facet rating of product reviews. In: ECIR'09 - 31st European Conference on Information Retrieval (Toulouse, FR, 7-9 April 2009). Proceedings, pp. 461 - 472. Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.). (Lecture Notes in Computer Science, vol. 5478). Springer Verlag, 2009.
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 emph{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.
Subject Text classification
Ordinal regression
Feature selection
Sentiment analysis
Opinion mining
I.2.6 Learning
I.5.2 Design Methodology. Classifier design and evaluation
H.3.1 Content Analysis and Indexing

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