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Mining Worse and Better Opinions

Unsupervised and Agnostic Aggregation of Online Reviews
  • Michela FazzolariEmail author
  • Marinella Petrocchi
  • Alessandro Tommasi
  • Cesare Zavattari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express. Our methodology is unsupervised, due to the fact that it does not rely on pre-labeled reviews, and it is agnostic, since it does not make any assumption about the domain or the language of the review content. We measure the adherence of a review content to the domain terminology extracted from a review set. First, we demonstrate the informativeness of the adherence metric with respect to the score associated with a review. Then, we exploit the metric values to group reviews, according to the opinions they express. Our experimental campaign has been carried out on two large datasets collected from Booking and Amazon, respectively.

Keywords

Social web mining Online reviews aggregation Adherence metric Domain terminology Contrastive approach 

Notes

Acknowledgments

Research partly supported by MSCA-ITN-2015-ETN grant agreement #675320 (European Network of Excellence in Cybersecurity) and by Fondazione Cassa di Risparmio di Lucca, financing the project Reviewland.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Informatics and Telematics (IIT-CNR)PisaItaly
  2. 2.LUCENSE SCaRLLuccaItaly

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