[1]肖宇晗,林慧苹,汪权彬,等.基于双特征嵌套注意力的方面词情感分析算法[J].智能系统学报,2021,16(1):142-151.[doi:10.11992/tis.202012024]
 XIAO Yuhan,LIN Huiping,WANG Quanbin,et al.An algorithm for aspect-based sentiment analysis based on dual features attention-over-attention[J].CAAI Transactions on Intelligent Systems,2021,16(1):142-151.[doi:10.11992/tis.202012024]
点击复制

基于双特征嵌套注意力的方面词情感分析算法

参考文献/References:
[1] LIU Bing. Sentiment analysis and opinion mining[J]. Synthesis lectures on human language technologies, 2012, 5(1):1-167.
[2] PANG B, LEE L. Opinion mining and sentiment analysis[M]. Foundations and Trends in Information Retrieval, 2008:1-135.
[3] HUANG Binxuan, OU Yanglan, CARLEY K M. Aspect level sentiment classification with attention-over-attention neural networks[C]//Proceedings of the 11th International Conference on Social, Cultural, and Behavioral Modeling. Washington, USA, 2018:197-206.
[4] SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada, 2014:3104-3112.
[5] TANG D, QIN B, FENG X, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics. Osaka, Japan, 2016:3298-3307.
[6] MA D, LI S, ZHANG X, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intel-ligence. Melbourne, Australia, 2017:4068-4074.
[7] XU Weidi, TAN Ying. Semi-supervised target-oriented sentiment classification[J]. Neurocomputing, 2019, 337:120-128.
[8] PETERS M, NEUMANN M, IYYER M, et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics.New Orleans, Louisiana, 2018:2227-2237.
[9] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Los Angeles, USA, 2017:6000-6010.
[10] RADFORD A, NARASIMHAN K, SALIMANS T, et al. Improving language understanding by generative p-re-training[EB/OL].[2019-5-10]. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.
[11] DEVLIN J, CHANG M- W, LEE K, et al. BERT:pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. Minneapolis, Minnesota, USA,2019:4171-4186.
[12] KARIMI A, ROSSI L, PRATI A. Adversarial training for aspect-based sentiment analysis with BERT[EB/OL].[2019-5-10]. https://arxiv.org/abs/2001.11316.
[13] SONG Youwei, WANG Jiahai, JIANG Tao, et al. Att-entional encoder network for targeted sentiment classi-fication[EB/OL].[2019-5-10]. https://arxiv.org/abs/1902.09314.
[14] JIANG Long, YU Mo, ZHOU Ming, et al. Target-dependent twitter sentiment classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. Portland, USA, 2011:151-160.
[15] CUI Y, CHEN Z, WEI S, et al. Attention-over-attention neural networks for reading comprehension[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Vancouver, Canada, 2017:593-602.
[16] PENNINGTON J, SOCHER R, MANNING C. Glove:global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar, 2014:1532-1543.
[17] ZENG Biqing, YANG Heng, XU Ruyang, et al. LCF:a local context focus mechanism for aspect-based sentiment classification[J]. Applied sciences, 2019, 9(16):1-22.
[18] PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. SemEval-2014 task 4:aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Dublin, Ireland, 2014:27-35.
[19] DONG Li, WEI Furu, TAN Chuanqi, et al. Adaptive recursive neural network for target-dependent twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, Maryland, 2014:49-54.
[20] KIRITCHENKO S, ZHU Xiaodan, CHERRY C, et al. NRC-Canada-2014:detecting aspects and sentiment in customer reviews[C]//Proceedings of the 8th International Workshop on Semantic Evaluation. Dublin, Ireland, 2014:437-442.
[21] CHEN Peng, SUN Zhongqian, BING Lidong, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark, 2017:452-461.
[22] XU H, LIU B, SHU L, et al. BERT post-training for review reading comprehension and aspect-based sentiment analysis[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. Minneapolis, Minnesota, USA, 2019:2324-2335.
[23] GLOROT X, BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Chia Laguna Resort, Italy, 2010:249-256.
[24] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:a simple way to prevent neural networks from overfitting[J]. The journal of machine learning research, 2014, 15(1):1929-1958.
[25] KINGMA D P, BA J. Adam:a method for stochastic optimization[C]//The 3rd International Conference for Learning Representations, San Diego. http://arxiv.org/abs/1412.6980.
[26] LI Xin, BING Lidong, LI Piji, et al. A unified model for opinion target extraction and target sentiment prediction[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1):6714-6721.
[27] HU M, PENG Y, HUANG Z, et al. Open-domain targeted sentiment analysis via span-based extraction and classification[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics.Florence, Italy, 2019:537 -546.
相似文献/References:
[1]赵文清,侯小可,沙海虹.语义规则在微博热点话题情感分析中的应用[J].智能系统学报,2014,9(1):121.[doi:10.3969/j.issn.1673-4785.201208020]
 ZHAO Wenqing,HOU Xiaoke,SHA Haihong.Application of semantic rules to sentiment analysis of microblog hot topics[J].CAAI Transactions on Intelligent Systems,2014,9():121.[doi:10.3969/j.issn.1673-4785.201208020]
[2]吴钟强,张耀文,商琳.基于语义特征的多视图情感分类方法[J].智能系统学报,2017,12(5):745.[doi:10.11992/tis.201706026]
 WU Zhongqiang,ZHANG Yaowen,SHANG Lin.Multi-view sentiment classification of microblogs based on semantic features[J].CAAI Transactions on Intelligent Systems,2017,12():745.[doi:10.11992/tis.201706026]
[3]曾碧卿,韩旭丽,王盛玉,等.层次化双注意力神经网络模型的情感分析研究[J].智能系统学报,2020,15(3):460.[doi:10.11992/tis.201812017]
 ZENG Biqing,HAN Xuli,WANG Shengyu,et al.Hierarchical double-attention neural networks for sentiment classification[J].CAAI Transactions on Intelligent Systems,2020,15():460.[doi:10.11992/tis.201812017]

备注/Memo

收稿日期:2020-12-15。
基金项目:国家重点研发计划项目(2018AAA0102301, 2018AAA0100302, 2018YFB1702900);国家自然科学基金项目(62076010)
作者简介:肖宇晗,硕士研究生,主要研究方向为深度学习、数据挖掘和自然语言处理;林慧苹,副教授,博士,主要研究方向为大数据分析、企业信息服务,主持和参与国家863计划、国家自然科学基金项目、国家重点研发计划项目等多项。发表学术论文20余篇;谭营,教授,博士生导师,主要研究方向为智能科学、计算智能与群体智能、机器学习、人工神经网络、群体机器人、大数据挖掘。烟花算法发明人。吴文俊人工智能科学技术成就奖创新三等奖获得者。发表学术论文 330 余篇,出版学术专著 12 部。
通讯作者:谭营. E-mail:ytan@pku.edu.cn

更新日期/Last Update: 2021-02-25
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com