›› 2015, Vol. 30 ›› Issue (1): 200-213.doi: 10.1007/s11390-015-1513-6

Special Issue: Data Management and Data Mining

• Data Management and Data Mining • Previous Articles     Next Articles

Review Authorship Attribution in a Similarity Space

Tie-Yun Qian1(钱铁云), Member, CCF, ACM, Bing Liu2(刘兵), Fellow, IEEE, Qing Li3(李青), Distinguished Member, CCF, Senior Member, IEEE, Jianfeng Si4(司建锋)   

  1. 1 State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;
    2 Department of Computer Science, University of Illinois at Chicago, Chicago 60607, U.S.A.;
    3 Multimedia Software Engineering Research Centre and Department of Computer Science, City University of Hong Kong Hong Kong, China;
    4 Data Analytics Department, Institute for Infocomm Research, Singapore 138632, Singapore
  • Received:2014-02-19 Revised:2014-11-14 Online:2015-01-05 Published:2015-01-05
  • About author:Tie-Yun Qian is an associate professor at the State Key Laboratory of Software Engineering at Wuhan University. She received her B.S. degree in computer science from Wuhan University of Technology in 1991, and her Ph.D. degree in computer science from Huazhong University of Science and Technology, Wuhan, in 2006. Her current research interests include text mining, web mining, and natural language processing. She has published over 20 papers in top conferences including ACL, EMNLP, SIGIR, etc. She is a member of CCF and ACM. She has served as program committee member of many leading conferences: WWW, COLING, DASFAA, WAIM, and APWeb.
  • Supported by:

    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.

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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