Cross-Language Sentiment Classification Algorithm Based on Dependency Analysis Parser and Weight on Property Probability
ZHANG Ling-Ling1, JI Jun-Zhong1, BEI Fei1, WU Chen-Sheng2
1.Beijing Key Laboratory of Multimedia and Intelligent Software Technology,Beijing University of Technology, Beijing 100124 2.Beijing Institute of Science and Technology Information, Beijing 100048
Abstract In the document-level sentiment classification methods,only the distribution information of emotion is taken into account, while the semantic emotion knowledge is ignored. To solve these problems,a cross-language sentiment classification algorithm based on the dependency analysis and property probability weight is proposed. Firstly, dependency relations are got by dependency relation parsing before translation. Then, based on the correlation between the distribution of dictionary polar and the document-level sentiment classification, the weight feature of property probability is merged into Naive bayesian classification to improve the classification effect. Finally, extensive experiments are performed on English datasets for training and standard Chinese datasets for testing. The results show that the proposed algorithm is superior to other existing algorithms in performance.
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2015, Vol. 28 
