[1] 魏韡, 向阳, 陈千. 中文文本情感分析综述[J]. 计算机应用, 2011, 31(12):3321-3323. (WEI W, XIANG Y, CHEN Q. Survey on Chinese text sentiment analysis[J]. Journal of Computer Applications, 2011, 31(12):3321-3323.) [2] TURNEY P D. Thumbs up or thumbs down?:semantic orientation applied to unsupervised classification of reviews[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2002:417-424. [3] NI X, XUE G, LING X, et al. Exploring in the weblog space by detecting informative and affective articles[C]//Proceedings of the 16th International Conference on World Wide Web. New York:ACM, 2007:281-290. [4] PANG B, LEE L, VAITHYANATHAN S. Thumbs up?:sentiment classification using machine learning techniques[C]//Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2002:79-86. [5] BENGIO Y, DUCHARME R, VINCENT P, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3:1137-1155. [6] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL].[2017-08-04]. http://www.surdeanu.info/mihai/teaching/ista555-spring15/readings/mikolov2013.pdf. [7] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[EB/OL].[2019-01-10]. http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf. [8] MNIH A, HINTON G E. A scalable hierarchical distributed language model[C]//Proceedings of the 21st International Conference on Neural Information Processing. New York:Curran Associates Inc., 2008:1081-1088. [9] KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P. A convolutional neural network for modelling sentences[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2014:655-665. [10] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proceeding. Stroudsburg, PA:Association for Computational Linguistics, 2014:1746-1751. [11] LEE J Y, DERNONCOURT F. Sequential short-text classification with recurrent and convolutional neural networks[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2016, 515-520. [12] CHO K, van MERRIENBOER B, GULCEHRE C, et al. Learning phrase representions using RNN encoder-decoder for statistical machine translation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.Stroudsburg, PA:Association for Computational Linguistics, 2014:1724-1734. [13] EBRAHIMI J, DOU D. Chain based RNN for relation classification[C]//Proceedings of the 2015 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2015:1244-1249. [14] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780. [15] ZHOU C, SUN C, LIU Z, et al. A C-LSTM neural network for text classification[EB/OL].[2019-01-09].https://arxiv.org/abs/1511.08630. [16] XIAO Z, LIANG P. Chinese sentiment analysis using bidirectional LSTM with word embedding[C]//Proceedings of the 2016 International Conference on Cloud Computing and Security, LNSC 10040. Berlin:Springer, 2016:601-610. [17] BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[EB/OL].[2018-03-20]. https://arxiv.org/pdf/1409.0473v7.pdf. [18] MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge, MA:MIT Press, 2014:2204-2212. [19] XU K, BA J, KIROS R, et al. Show, attend and tell:neural image caption generation with visual attention[EB/OL].[2018-03-20]. https://arxiv.org/pdf/1502.03044.pdf. [20] LUONG M PHAM H, MANNING C D. Effective approaches to attention-based neural machine translation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2015:1412-1421. [21] 胡荣磊, 芮璐, 齐筱, 等. 基于循环神经网络和注意力模型的文本情感分析[J/OL]. 计算机应用研究, 2019, 36(11).[2018-12-10]. http://www.arocmag.com/article/02-2019-11-025.html. (HU R L, RUI L, QI X, et al. Text sentiment analysis based on recurrent neural network and attention model[J/OL]. Application Research of Computers, 2019, 36(11).[2018-12-10]. http://www.arocmag.com/article/02-2019-11-025.html.) [22] 王伟, 孙玉霞, 齐庆杰, 等. 基于BiGRU-Attention神经网络的文本情感分类模型[J/OL]. 计算机应用研究, 2018, 36(12)[2018-12-10]. http://www.arocmag.com/article/02-2019-12-045.html (WANG W, SUN Y X, QI Q J, et al. Text sentiment classification model based on BiGRU-Attention neural network[J/OL]. Application Research of Computers, 2018, 36(12)[2018-12-10]. http://www.arocmag.com/article/02-2019-12-045.html.) [23] 陈洁, 邵志清, 张欢欢, 等. 基于并行混合神经网络模型的短文本情感分析[J/OL]. 计算机应用, 2019.[2018-12-10]. http://kns.cnki.net/kcms/detail/51.1307.TP.20190329.1643.008.html. (CHEN J, SHAO Z Q, ZHANG H H, et al. Short text sentiment analysis based on parallel hybrid neural network model[J/OL]. Journal of Computer Applications, 2019.[2018-12-10]. http://kns.cnki.net/kcms/detail/51.1307.TP.20190329.1643.008.html.) [24] 常丹, 王玉珍. 基于SVM的用户评论情感分析方法研究[J]. 枣庄学院学报, 2019, 36(2):73-78. (CHANG D, WANG Y Z. Research on the method of user comment sentiment analysis based on SVM[J]. Journal of Zaozhuang University, 2019, 36(2):73-78.) [25] 王煜涵, 张春云, 赵宝林, 等. 卷积神经网络下的Twitter文本情感分析[J]. 数据采集与处理, 2018, 33(5):921-927. (WANG Y H, ZHANG C Y, ZHAO B L, et al. Sentiment analysis of twitter data based on CNN[J]. Journal of Data Acquisition and Processing, 2018, 33(5):921-927.) |