ZHANG Yangsen, JIANG Yuru, TONG Yixuan. Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network[J]. Chinese Journal of Electronics, 2016, 25(4): 601-607. doi: 10.1049/cje.2016.07.002
Citation: ZHANG Yangsen, JIANG Yuru, TONG Yixuan. Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network[J]. Chinese Journal of Electronics, 2016, 25(4): 601-607. doi: 10.1049/cje.2016.07.002

Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network

doi: 10.1049/cje.2016.07.002
Funds:  This work is supported by the National Natural Science Foundation of China (No.61370139), The Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (No.IDHT20130519), and Scientific Research Project of Beijing Municipal Commission of Education (No.KM201411232014).
  • Received Date: 2015-11-01
  • Rev Recd Date: 2016-02-10
  • Publish Date: 2016-07-10
  • The sentiment classification of Chinese Microblog is a meaningful topic. Many studies has been done based on the methods of rule and word-bag, and to understand the structure information of a sentence will be the next target. We proposed a sentiment classification method based on Recurrent neural network (RNN). We adopted the technology of distributed word representation to construct a vector for each word in a sentence; then train sentence vectors with fixed dimension for different length sentences with RNN, so that the sentence vectors contain both word semantic features and word sequence features; at last use softmax regression classifier in the output layer to predict each sentence's sentiment orientation. Experiment results revealed that our method can understand the structure information of negative sentence and double negative sentence and achieve better accuracy. The way of calculating sentence vector can help to learn the deep structure of sentence and will be valuable for different research area.
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  • Bo Pang and L. Lee, "Opinion mining and sentiment analysis", Foundation and Trends in Information Retrieval, Vol.1, pp.1-135, 2008.
    Bo Pang and L. Lee, "Thumbs up? Sentiment classification using machine learning techniques", Proc. of EMNLP, pp.79-86, 2002.
    Adam Bermingham and Alan Smeaton, "Classifying sentiment in microblogs:Is brevity an advantage", Proc. of the 19th ACM International Conference on Information and Knowledge Management, pp.1833-1836, 2010.
    PANG Lei, LI Shoushan and ZHOU Guodong, "Sentiment classification method of chinese micro-blog based on emotional knowledge", Computer Engineering, Vol.38, No.13, pp.156-158, 162, July,2012. (in Chinese)
    XU Linhong, Lin Hongfei and PAN Yu, et al., "Constructing the affective lexicon ontology", Journal of The China Society for Scientific and Technical Information, Vol.27, No.2, pp.180-185, 2008. (in Chinese)
    YOU Jianping, "Micro-blog sentiment analysis based on semantic sentiment space model", Master degree thesis, Jinan University, 2012. (in Chinese)
    LU Yujie, "Chinese twitter sentiment analysis", Master degree thesis, East China Normal University, 2013. (in Chinese)
    XIE Lixing, ZHOU Ming and SUN Maosong, "Hierarchical structure based hybrid approach to sentiment analysis of chinese micro Blog and its feature extraction", Journal of Chinese Information Processing, pp.73-83, 2012. (in Chinese)
    OUYANG Chunping, YANG Xiaohua, LEI Longyan, et al., "Multi-strategy approach for fine-grained sentiment analysis of Chinese microblog", Acta Scientiarum Naturalium Universitatis Pekinensis, pp.67-72, 2014. (in Chinese)
    CHEN Tao, XU Ruifeng, WU Mingfen, et al., "A sentiment classification approach based on sentence framework", Journal of Chinese Information Processing, pp.67-74, 2013. (in Chinese)
    Tomas Mikolov, Kai Chen, Greg Corrado, et al., "Efficient estimation of word representations in vector space", Proc. of Workshop at ICLR, 2013.
    Tomas Mikolov, Ilya Sutskever and Kai Chen, "Distributed representations of words and phrases and their compositionality", Proc. of NIPS, 2013.
    J. Bergstra, O. Breuleux and F. Bastien, "Theano:A CPU and GPU math expression compiler", Proc. of the Python for Scientific Computing Conference (SciPy), 2010.
    Yoshua Bengio, Rejean Ducharme and Pascal Vincent. "A neural probabilistic language model", Journal of Machine Learning Research (JMLR), pp.1137-1155, 2003.
    MAO Liuping, WANG Yaonan, SUN Wei and DAI Yuxing, "An adaptive control using recurrent fuzzy neural network", Acta Electronica Sinica, Vol.34, No.12, pp.2285-2287, 2006. (in Chinese)
    ZUO Bin, HU Yunan and LI Jing, "Research on extremum seeking algorithm based on chaotic annealing recurrent neural network with parameter disturbances and its application", Acta Electronica Sinica, Vol.37, No.12, pp.2651-2656, 2009. (in Chinese)
    Tomas Mikolov, "Statistical language models based on neural networks", Ph.D. Thesis, Brno University of Technology, 2012.
    Ronan Collobert, Jason Weston and Léon Bottou, et al., "Natural language processing (almost) from scratch", Journal of Machine Learning Research (JMLR), pp.2493-2537, 2011.
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