Feature Polymeric Topology Model for Short-Text Sentiment Classification
HU Yang1,FENG Xupeng2,HUANG Qingsong1,3,FU Xiaodong1,LIU Li1,LIU Lijun1
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming,Yunnan 650500,China; 2. Educational Technology and Network Center, Kunming University of Science and Technology, Kunming,Yunnan 650500,China; 3. Yunnan Key Laboratory of Computer Technology Applications, Kunming,Yunnan 650500,China
Abstract：Short-text has some peculiarities: extreme sparsity, disperse features and so on, which leads to inferior sentiment classification on short-text. To solve this problem, we propose the feature polymeric topology model for short-text sentiment classification. The model integrates mutual information among features, similarity of sentiment orientation and topic ascription difference into the sentiment features correlation. Then this correlation is employed to establish topology polymeric graph, in which the strongly connected components are assumed as the most similar sentiment features. Finally, the polymeric topology model supplements the training feature set with similar features from the unlabeled corpora, and reduces dimension of training space at same time. In experiment,the proposed model can improve the presicion and recall by 0.03 and 0.027, respectively.
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2016, Vol. 30 
