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Using Reviewer Information to Improve Performance of Low-Quality Review Detection

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Computational Linguistics and Intelligent Text Processing (CICLing 2018)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13397))

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Abstract

The drastic increase of user-generated contents has exhibited a rich source for mining opinions. Unfortunately, the quality of user-generated content varies significantly from excellent to meaningless, which by general estimation, causes a great deal of difficulty in mining-related applications. In the field of low-quality review detection, many previous approaches have individually detected low-quality reviews by using the intrinsic features of the review. However, no systematic study measuring the significance of reviewer information for detecting low-quality reviews has been previously done. In this paper, the importance of reviewer information when predicting review quality is studied and how to exploit it to build low-quality review detection models is determined. The experimental results on two different domains show that reviewer information does matter when modeling and predicting the quality of reviews. It is also shown that significant performance improvements can be achieved if the reviewer information is integrated with the intrinsic features of the reviews. These findings are of the essence in solving the low-quality review detection problem and in developing review-based opinion mining applications.

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Authors and Affiliations

  1. Lenovo Research, No. 6 Shangdi West Road, Haidian District, Beijing, China

    Qingliang Miao, Changjian Hu & Feiyu Xu

Authors
  1. Qingliang Miao
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  2. Changjian Hu
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  3. Feiyu Xu
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Corresponding author

Correspondence to Qingliang Miao .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Miao, Q., Hu, C., Xu, F. (2023). Using Reviewer Information to Improve Performance of Low-Quality Review Detection. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13397. Springer, Cham. https://doi.org/10.1007/978-3-031-23804-8_30

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  • DOI: https://doi.org/10.1007/978-3-031-23804-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23803-1

  • Online ISBN: 978-3-031-23804-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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