Chinese Microblog Sentiment Classification Based on Convolutional Neural Network
LIAO Xiangwen1,2, ZHANG Liyao1,2, SONG Zhigang3, CHENG Xueqi4, CHEN Guolong1,2
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 2.Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University, Fuzhou 350116 3.Information Construction Office, Fuzhou University, Fuzhou 350116 4.Key Laboratory of Network Data Science and Engineering, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190
Abstract：To tackle the problems of the underutilization of context, the sparseness of data and the dependence on human-designed features in existing Chinese microblog sentiment classification methods, a Chinese microblog sentiment classification method based on convolutional neural network is proposed. Firstly, microblog messages are extended using the interaction context, and then they are initialized with dense vectors in the low-dimension space. Secondly, a convolutional neural network model is constructed for extracting and combining features. Finally, the sentiment of each microblog message is estimated by softmax function. Experimental results show that compared with baselines, the proposed method obtains higher accuracies and F1 values.
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2016, Vol. 29 
