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Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text

Speaker

Taras Zagibalov

Affilliation

Sussex

Abstract

I will describe 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 supervised classifiers and sometimes better, up to an F1 of 92%.

[Practice talk for COLING 2008]

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