Multi-aspect opinion polling from textual reviews

  • Authors:
  • Jingbo Zhu;Huizhen Wang;Benjamin K. Tsou;Muhua Zhu

  • Affiliations:
  • Northeastern University, Shenyang, China;Northeastern University, Shenyang, China;City University of Hong Kong, Hong Kong, Hong Kong;Northeastern University, Shenyang, China

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

This paper presents an unsupervised approach to aspect-based opinion polling from raw textual reviews without explicit ratings. The key contribution of this paper is three-fold. First, a multi-aspect bootstrapping algorithm is proposed to learn from unlabeled data aspect-related terms of each aspect to be used for aspect identification. Second, an unsupervised segmentation model is proposed to address the challenge of identifying multiple single-aspect units in a multi-aspect sentence. Finally, an aspect-based opinion polling algorithm is presented. Experiments on real Chinese restaurant reviews show that our opinion polling method can achieve 75.5% precision performance.