PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Baccianella S., Esuli A., Sebastiani F. StarTrack: the next generation (of product review management tools). Technical report, 2009.
 
 
Abstract
(English)
Online product reviews are increasingly being recognized as a goldmine of information for determining product and brand positioning, and more and more companies look for ways of digging this goldmine for nuggets of knowledge that they can then bring to bear in decision making. We present a software system, called StarTrack, that automatically rates a product review according to a number of "stars", i.e., according to how positive it is. In other words, given a text-only review (i.e., one with no explicit star-rating attached), StarTrack attempts to guess the star-rating that the reviewer would have attached to the review. StarTrack is thus useful for analyzing unstructured word-of-mouth on products, such as the comments and reviews about products that are to be found in spontaneous discussion forums, such as newsgroups, blogs, and the like. StarTrack is based on "machine learning" technology, and as such does not require any re-programming for porting it from one product domain to another. Based on the star-ratings it attributes to reviews, StarTrack can also rank the products in a given set according to how favourably they have been reviewed by consumers. We present controlled experiments in which we evaluate, on two large sets of product reviews crawled from the Web, the accuracy of StarTrack at (i) star-rating reviews and (ii) ranking the reviewed products based on the attributed star-ratings.
Subject Ordinal regression
Product reviews
Opinion mining
Sentiment analysis
Text classification
I.2.6 Learning (K.3.2)
I.5.2 Design Methodology. Classifier design and evaluation


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