Automatic seed word selection for unsupervised sentiment classification of Chinese text

  • Authors:
  • Taras Zagibalov;John Carroll

  • Affiliations:
  • University of Sussex, Brighton, UK;University of Sussex, Brighton, UK

  • Venue:
  • COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
  • Year:
  • 2008

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Abstract

We describe and evaluate a new method of automatic seed word selection for un-supervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not require any annotated training data; it only requires information about commonly occurring negations and adverbials. Unsupervised techniques are promising for this task since they avoid problems of domain-dependency typically associated with supervised methods. The results obtained are close to those of supervised classifiers and sometimes better, up to an F1 of 92%.