Paper: Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text

ACL ID C08-1135
Title Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

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 re- quire any annotated training data; it only requires information about commonly oc- curring negations and adverbials. Unsu- pervised techniques are promising for this task since they avoid problems of do- main-dependency typically associated with supervised methods. The results ob- tained are close to those of supervised classifiers and sometimes better, up to an F1 of 92%.