New Generation Computing

, Volume 31, Issue 1, pp 47–70 | Cite as

StarTrack: The Next Generation (of Product Review Management Tools)

  • Stefano Baccianella
  • Andrea Esuli
  • Fabrizio Sebastiani
Article

Abstract

Online product reviews are increasingly being recognized as a gold mine of information for determining product and brand positioning, and more and more companies look for ways of digging this gold mine 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 analysing 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 subsequently 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 automatically attributed star-ratings.

Keywords

Product reviews Sentiment analysis Sentiment lexicons Ordinal regression Text classification 

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Copyright information

© Ohmsha and Springer Japan 2013

Authors and Affiliations

  • Stefano Baccianella
    • 1
  • Andrea Esuli
    • 1
  • Fabrizio Sebastiani
    • 1
  1. 1.Istituto di Scienza e Tecnologia dell’InformazioneConsiglio Nazionale delle RicerchePisaItaly

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