Unsupervised Classification of Sentiment and Objectivity in Chinese Text
نویسندگان
چکیده
We address the problem of sentiment and objectivity classification of product reviews in Chinese. Our approach is distinctive in that it treats both positive / negative sentiment and subjectivity / objectivity not as distinct classes but rather as a continuum; we argue that this is desirable from the perspective of would-be customers who read the reviews. We use novel unsupervised techniques, including a one-word 'seed' vocabulary and iterative retraining for sentiment processing, and a criterion of 'sentiment density' for determining the extent to which a document is opinionated. The classifier achieves up to 87% F-measure for sentiment polarity detection.
منابع مشابه
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-de...
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