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Unsupervised Topic Extraction for the Reviewer’s Assistant

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Book cover Research and Development in Intelligent Systems XXIX (SGAI 2012)

Abstract

User generated reviews are now a familiar and valuable part of most ecommerce sites since high quality reviews are known to influence purchasing decisions. In this paper we describe work on the Reviewer’s Assistant (RA), which is a recommendation system that is designed to help users to write better reviews. It does this by suggesting relevant topics that they may wish to discuss based on the product they are reviewing and the content of their review so far. We build on prior work and describe an unsupervised topic extraction module for the RA system that enhances the system’s ability to automatically adapt to new content categories and application domains. Our main contribution includes the results of a controlled, live-user study to show that the RA system is capable of supporting users to create reviews that enjoy higher quality ratings than Amazon’s own high quality reviews, even without using manually created topic models.

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Authors and Affiliations

  1. Centre for Sensor Web Technologies, University College Dublin, Dublin, Ireland

    Ruihai Dong, Markus Schaal, Michael P. O’Mahony, Kevin McCarthy & Barry Smyth

Authors
  1. Ruihai Dong
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  2. Markus Schaal
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  3. Michael P. O’Mahony
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  4. Kevin McCarthy
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  5. Barry Smyth
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Corresponding author

Correspondence to Ruihai Dong .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Whitepost Lane The Lilacs, Portsmouth, PO1 3AH, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering & Mathe, University of Brighton, Lewes Road, Brighton, BN2 4GJ, West Sussex, United Kingdom

    Miltos Petridis

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© 2012 Springer-Verlag London

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Dong, R., Schaal, M., O’Mahony, M.P., McCarthy, K., Smyth, B. (2012). Unsupervised Topic Extraction for the Reviewer’s Assistant. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_25

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  • DOI: https://doi.org/10.1007/978-1-4471-4739-8_25

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4738-1

  • Online ISBN: 978-1-4471-4739-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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