Application of Chinese sentiment categorization to digital products reviews

Sentiment categorization have been widely explored in many fields, such as government policy, information monitoring, product tracking, etc. This paper adopts k-NN, Naive Bayes and SVM classifiers to categorize sentiments contained in on-line Chinese reviews on digital products. Our experimental results show that combining the words and phrases with sentiment orientation as hybrid features, SWM classifier achieves an accuracy of 96,47%, which is words of all parts of speech as features.