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Predicting Online Review Scores Across Reviewer Categories

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Intelligent Data Engineering and Automated Learning – IDEAL 2018 (IDEAL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11314))

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Abstract

In this paper, we propose and test an approach based on regression models, to predict the review score of an item, across different reviewer categories. The analysis is based on a public dataset with more than 2.5 million hotel reviews, belonging to five specific reviewers’ categories. We first compute the relation between the average scores associated with the different categories and generate the corresponding regression model. Then, the extracted model is used for prediction: given the average score of a hotel according to a reviewer category, it predicts the average score associated with another category.

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Notes

  1. 1.

    http://www.booking.com - All URLs have been lastly accessed on July 22, 2018.

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Acknowledgements

Partially funded by Fondazione Cassa Risparmio Lucca, under the ReviewLand project; and MIUR, under grant “Dipartimenti di eccellenza 2018–2022”, Computer Science Dept., Sapienza University, Rome.

Author information

Authors and Affiliations

  1. Istituto di Informatica e Telematica, CNR, Pisa, Italy

    Michela Fazzolari & Marinella Petrocchi

  2. Dipartimento di Informatica, Sapienza Università di Roma, Rome, Italy

    Angelo Spognardi

Authors
  1. Michela Fazzolari
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  2. Marinella Petrocchi
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  3. Angelo Spognardi
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Corresponding author

Correspondence to Michela Fazzolari .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Fazzolari, M., Petrocchi, M., Spognardi, A. (2018). Predicting Online Review Scores Across Reviewer Categories. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_73

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  • DOI: https://doi.org/10.1007/978-3-030-03493-1_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03492-4

  • Online ISBN: 978-3-030-03493-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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