Review Authorship Attribution in a Similarity Space

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• 作者：Tie-Yun Qian (1)
  Bing Liu (2)
  Qing Li (3)
  Jianfeng Si (4)
  
  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
  
• 关键词：authorship attribution ; supervised learning ; similarity space
• 刊名：Journal of Computer Science and Technology
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：30
• 期：1
• 页码：200-213
• 全文大小：708 KB
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• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Software Engineering
  Theory of Computation
  Data Structures, Cryptology and Information Theory
  Artificial Intelligence and Robotics
  Information Systems Applications and The Internet
  Chinese Library of Science
  
• 出版者：Springer Boston
• ISSN：1860-4749

文摘

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.