Page 176 - 《软件学报》2021年第9期
P. 176
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.