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Review Authorship Attribution in a Similarity Space

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Abstract

Authorship attribution, also known as authorship classification, is the problem of identifying the authors (reviewers) of a set of documents (reviews). The common approach is to build a classifier using supervised learning. This approach has several issues which hurts its applicability. First, supervised learning needs a large set of documents from each author to serve as the training data. This can be difficult in practice. For example, in the online review domain, most reviewers (authors) only write a few reviews, which are not enough to serve as the training data. Second, the learned classifier cannot be applied to authors whose documents have not been used in training. In this article, we propose a novel solution to deal with the two problems. The core idea is that instead of learning in the original document space, we transform it to a similarity space. In the similarity space, the learning is able to naturally tackle the issues. Our experiment results based on online reviews and reviewers show that the proposed method outperforms the state-of-the-art supervised and unsupervised baseline methods significantly.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Tie-Yun Qian

  2. Department of Computer Science, University of Illinois at Chicago, Chicago, 60607, U.S.A

    Bing Liu

  3. Multimedia Software Engineering Research Centre and Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Qing Li

  4. Data Analytics Department, Institute for Infocomm Research, Singapore, 138632, Singapore

    Jianfeng Si

Authors
  1. Tie-Yun Qian
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  2. Bing Liu
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  3. Qing Li
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  4. Jianfeng Si
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Corresponding author

Correspondence to Qing Li.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61272275, 61232002, 61272110, 61202036, 61379004, 61472337, and 61028003, and the 111 Project of China under Grant No. B07037.

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Qian, TY., Liu, B., Li, Q. et al. Review Authorship Attribution in a Similarity Space. J. Comput. Sci. Technol. 30, 200–213 (2015). https://doi.org/10.1007/s11390-015-1513-6

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  • DOI: https://doi.org/10.1007/s11390-015-1513-6

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