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.
Fund:Supported by Young Scientists Fund of National Natural Science Foundation of China (No.61300105), Joint Project of Doctoral Fund of Ministry of Education of China (No.2012351410010), Major Science and Technology Projects of Fujian Province(No.2013H6012), Open Project of Key Laboratory of Network Data Science & Technology of Chinese Academy of Sciences (No.CASNDST20140X), CERNET Innovation Project (No.NGII20150901)
About author:: (LIAO Xiangwen(Corresponding author), born in 1980, Ph.D., associate professor. His research interests include opinion mining and sentiment analysis.)(ZHANG Liyao, born in 1991, master student. Her research interests include opinion mining and sentiment analysis.)(SONG Zhigang, born in 1972, bachelor, senior engineer. His research interests include network engineering.)(CHENG Xueqi, born in 1971, Ph.D., professor. His research interests include big data analysis and mining.)(CHEN Guolong, born in 1965, Ph.D., professor. His research interests include intelligence information processing.)
[1] PANG B, LEE L. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2008, 2(1/2): 1-135. [2] 许洪波,廖祥文,王素格,等.中文倾向性分析评测技术报告 [C/OL]. [2016-05-30]. http://nlpr-web.ia.ac.cn/2008papers/gnhy/nh10.pdf. (XU H B, LIAO X W, WANG S G, et al. Technical Reports of Chinese Opinion Analysis Evaluation[C/OL]. [2016-05-30]. http://nlpr-web.ia.ac.cn/2008papers/gnhy/nh10.pdf.) [3] YANG B S, CARDIE C. Context-Aware Learning for Sentence-Level Sentiment Analysis with Posterior Regularization // Proc of the 52nd Annual Meeting of the Association for Computational Linguistics. New York, USA: ACM, 2014: 325-335. [4] VANZO A, CROCE D, BASILI R. A Context-Based Model for Sentiment Analysis in Twitter [C/OL]. [2016-05-30]. http://www.aclweb.org/anthology/C/C/4/C14/C14-1221.pdf. [5] TAN C H, LEE L, TANG J, et al. User-Level Sentiment Analysis Incorporating Social Networks // Proc of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2011: 1397-1405. [6] HINTON G E. Learning Distributed Representations of Concepts // Proc of the 8th Annual Conference of the Cognitive Science Society. Cambridge, USA: MIT Press, 1986, I: 1-12. [7] MIKOLOV T, KARAFI T M, BURGET L, et al. Recurrent Neural Network Based Language Model [C/OL]. [2016-05-30]. http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf. [8] SOCHER R, LIN C Y, NG A Y, et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks // Proc of the International Conference on Machine Learning. Washington, USA:IEEE, 2011: 129-136. [9] COLLOBERT R, WESTON J, BOTTOU L, et al. Natural Language Processing(Almost) from Scratch. Journal of Machine Learning Research, 2011, 12: 2493-2537. [10] TANG D Y, WEI F R, YANG N, et al. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification // Proc of the 52nd Annual Meeting of the Association for Computational Linguistics. New York, USA: ACM, 2014: 1555-1565. [11] 梁 军,柴玉梅,原慧斌,等.基于深度学习的微博情感分析.中文信息学报, 2014, 28(5): 155-161. (LIANG J, CHAI Y M, YUAN H B, et al. Deep Learning for Chinese Micro-blog Sentiment Analysis. Journal of Chinese Information Processing, 2014, 28(5): 155-161.) [12] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient Estimation of Word Representations in Vector Space[J/OL]. [2016-05-30]. http://arxiv.org/pdf/1301.3781v3.pdf. [13] YU L, ASUR S, HUBERMAN B A. What Trends in Chinese Social Media [C/OL]. [2016-05-30]. http://www.hpl.hp.com/research/scl/papers/chinatrends/china_trends.pdf. [14] BOTTOU L. Stochastic Gradient Descent Tricks[J/OL]. [2016-05-30]. http://leon.bottou.org/publications/pdf/tricks-2012.pdf. [15] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving Neural Networks by Preventing Co-adaptation of Feature Detectors. Computer Science, 2012, 3(4): 212-223. [16] JIANG L, YU M, ZHOU M, et al. Target-Dependent Twitter Sentiment Classification // Proc of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. New York, USA: Association for Computational Linguistics, 2011: 151-160. [17] LAI S, XU L, LIU K, et al. Recurrent Convolutional Neural Networks for Text Classification // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2015: 2267-2273.
2016, Vol. 29 
