Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text

  title={Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text},
  author={Taras Zagibalov and John Carroll},
We describe and evaluate a new method of automatic seed word selection for unsupervised 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… CONTINUE READING
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