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COLING
2008

Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text

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Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text
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 supervised classifiers and sometimes better, up to an F1 of 92%.
Taras Zagibalov, John Carroll
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Taras Zagibalov, John Carroll
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