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%.

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Taras Zagibalov, John Carroll
• Keywords: Automatic Seed Word, COLING 2008, Computational Linguistics, Unsupervised Sentiment Classification, Unsupervised Techniques