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.
王海燕, 胡学钢, 李培培. 基于向量表示和标签传播的半监督短文本数据流分类算法[J]. 模式识别与人工智能, 2018, 31(7): 634-642.
WANG Haiyan , HU Xuegang , LI Peipei. Semi-supervised Short Text Stream Classification Based on Vector Representation and Label Propagation. , 2018, 31(7): 634-642.
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2018, Vol. 31 
