Opinion Target Network and Bootstrapping Method for Chinese Opinion Target Extraction

  • Yunqing Xia
  • Boyi Hao
  • Kam-Fai Wong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)


Opinion mining systems suffer a great loss when unknown opinion targets constantly appear in newly composed reviews. Previous opinion target extraction methods typically consider human-compiled opinion targets as seeds and adopt syntactic/statistic patterns to extract opinion targets. Three problems are worth noting. First, the manually defined opinion targets are too large to be good seeds. Second, the list that maintains seeds is not powerful to represent relationship between the seeds. Third, one cycle of opinion target extraction is barely able to give satisfactory performance. As a result, coverage of the existing methods is rather low. In this paper, the opinion target network (OTN) is proposed to organize atom opinion targets of component and attribute in a two-layer graph. Based on OTN, a bootstrapping method is designed for opinion target extraction via generalization and propagation in multiple cycles. Experiments on Chinese opinion target extraction show that the proposed method is effective.


Opinion target extraction opinion mining information extraction 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yunqing Xia
    • 1
  • Boyi Hao
    • 1
    • 2
  • Kam-Fai Wong
    • 3
  1. 1.Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.Department of System Engineering and Engineering ManagementThe Chinese University of Hong Kong, ShatinHong Kong

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