Semi-supervised Short Text Stream Classification Based on Vector Representation and Label Propagation
WANG Haiyan1 , HU Xuegang1,2 , LI Peipei1,2
1.School of Computer and Information, Hefei University of Technology, Hefei 230601 2.Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei 230009
Abstract The huge volume of short text streams produced by social Network is fast, high-volume and it contains concept drift, short length of texts and massive unlabeled data. Therefore, a semi-supervised short text stream classification algorithm based on vector representation and label propagation is proposed in this paper to classify short text stream with a few labeled data. Besides, to adapt to the concept drift, a concept drift detection algorithm based on clusters is proposed. Experimental results on real short text streams show that the proposed algorithm improves the classification accuracy and the macro average compared with classical semi-supervised classification algorithms and semi-supervised data stream classification algorithms, and it adapts to the concept drift quickly in data stream.
Fund:Supported by National Key Research and Development Program of China(No.2016YFC0801406), National Natural Science Foundation of China(No.61503112,61673152)
Corresponding Authors:
LI Peipei(Corresponding author), Ph.D., associate professor. Her research interests include data stream mining and knowledge engineering.
About author:: WANG Haiyan, master student. Her research interests include short text stream cla-ssification.HU Xuegang, Ph.D., professor. His re-search interests include data mining and know-ledge engineering.
WANG Haiyan,HU Xuegang,LI Peipei. Semi-supervised Short Text Stream Classification Based on Vector Representation and Label Propagation[J]. , 2018, 31(7): 634-642.
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2018, Vol. 31 
