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2800                                 Journal of Software  软件学报 Vol.32, No.9,  September 2021

         [33]    Tang D, Qin B, Liu T. Learning semantic representations of users and products for document level sentiment classification. In: Proc.
             of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int’l Joint Conf. on Natural Language
             Processing. Stroudsburg: Association for Computational Linguistics, 2015. 1014−1023. [doi: 10.3115/v1/P15-1098]
         [34]    Xu J, Chen D, Qiu X, Huang X. Cached long short-term memory neural networks for document-level sentiment classification. In:
             Proc. of the 2016 Conf. on Empirical Methods  in  Natural  Language Processing. Stroudsburg:  Association  for  Computational
             Linguistics, 2016. 1660−1669. [doi: 10.18653/v1/D16-1172]
         [35]    Chen H, Sun M, Tu C, Lin Y, Liu Z. Neural sentiment classification with user and product attention. In: Proc. of the 2016 Conf. on
             Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016. 1650−1659.
             [doi: 10.18653/v1/D16-1171]
         [36]    Pennington J, Socher  R,  Manning  C.  Glove: Global vectors for  word representation. In: Proc. of the 2014  Conf. on  Empirical
             Methods in Natural Language Processing (EMNLP). Stroudsburg: Association for Computational Linguistics, 2014. 1532−1543.
             [doi: 10.3115/v1/D14-1162]
         [37]    Liu Y, Bi JW, Fan ZP. A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the
             support vector machine (SVM) algorithm. Information Sciences, 2017,394:38−52. [doi: 10.1016/j.ins.2017.02.016]
         [38]    Zhao W, Ye J, Yang M, Lei Z, Zhang S, Zhao Z. Investigating capsule networks with dynamic routing for text classification. In:
             Proc. of the 2018 Conf. on Empirical Methods  in  Natural  Language Processing. Stroudsburg:  Association  for  Computational
             Linguistics, 2018. 3110−3119. [doi: 10.18653/v1/D18-1350]
         [39]    Greff  K, Srivastava  RK, Koutník J,  Steunebrink  BR, Schmidhuber J.  LSTM:  A search space odyssey. IEEE  Trans. on  Neural
             Networks and Learning Systems, 2017,28(10):2222−2232. [doi: 10.1109/TNNLS.2016.2582924]

         附中文参考文献:
          [8]  陈钊,徐睿峰,桂林,陆勤.结合卷积神经网络和词语情感序列特征的中文情感分析.中文信息学报,2015,29(6):172−178. http://jcip.
             cipsc.org.cn/CN/Y2015/V29/I6/172 [doi: CNKI:SUN:MESS.0.2015-06-024]
         [10]  裴颂文,王露露.基于注意力机制的文本情感倾向性研究.计算机工程与科学,2019,41(2):344−353. [doi: CNKI:SUN:JSJK.0.2019-
             02-023]
         [13]  黄发良,冯时,王大玲,于戈.基于多特征融合的微博主题情感挖掘.计算机学报,2017,40(4):872−888. [doi: 10.11897/SP.J.1016.
             2017.00872]
         [14]  黄发良,于戈,张继连,李超雄,元昌安,卢景丽.基于社交关系的微博主题情感挖掘.软件学报,2017,28(3):694−707. http://www.jos.
             org.cn/1000-9825/5157.htm [doi: 10.13328/j.cnki.jos.005157]
         [24]  梁斌,刘全,徐进,周倩,章鹏.基于多注意力卷积神经网络的特定目标情感分析.计算机研究与发展,2017,54(8):1724−1735. [doi:
             10.7544/issn1000-1239.2017.20170178]
         [25]  关鹏飞,李宝安,吕学强,周建设.注意力增强的双向 LSTM 情感分析.中文信息学报,2019,33(2):105−111. http://jcip.cipsc.org.cn/
             CN/Y2019/V33/I2/105 [doi: CNKI:SUN:MESS.0.2019-02-017]


                       李卫疆(1969-),男,博士,教授,主要研究                      余正涛(1970-),男,博士,教授,博士生导
                       领域为自然语言处理,信息检索.                              师,CCF 高级会员,主要研究领域为自然语
                                                                    言处理,机器翻译,信息检索.



                       漆芳(1994-),女,硕士,主要研究领域为自
                       然语言处理,情感分析.
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