[1] |
刘全, 梁斌, 徐进, 等. 一种用于基于方面情感分析的深度分层网络模型[J]. 计算机学报, 2018, 41(12): 3-18.
|
[2] |
孙小婉, 王英, 王鑫, 等. 面向双注意力网络的特定方面情感分析模型[J]. 计算机研究与发展, 2019, 11: 2384-2395.
|
[3] |
Kiritchenko S, Zhu X, Cherry C, et al. Nrc-canada-2014: detecting aspects and sentiment in customer reviews [C]// Proceedings of the 8th International Workshop on Semantic Evaluation (SEMEVAL 2014). 2014: 437-442.
|
[4] |
Manek A S, Deepa S P, Chandra M M, et al. Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[J]. World Wide Web-internet & Web Information Systems, 2019, 20(2): 135-154.
|
[5] |
Liang B, Liu Q, Xu J, et al. Aspect-based sentiment analysis based on multi-attentionCNN[J]. Journal of Computer Research and Development, 2017, 54(8): 1724-1735.
|
[6] |
Yao L, Rong H T, Chu C, et al. Mining aspects in online comments with attention and Bi-LSTM [C]// 2019 5th International Conference on Big Data Computing and Communications (BIGCOM). 2019: 288-292.
|
[7] |
Meng W, Wei Y, Liu P, et al. Aspect based sentiment analysis with feature enhanced attention CNN-BiLSTM[J]. IEEE Access, 2019(7): 167240-167249.
|
[8] |
Luong M T, 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 (EMNLP). 2015: 1412-1421.
|
[9] |
Jiang L, Yu M, Zhou M, et al. Target-dependent twitter sentiment classification [C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL). 2011: 151-160.
|
[10] |
Do H H, Prasad P W C, Maag A, et al. Deep learning for aspect-based sentiment analysis: a comparative review[J]. Expert Systems with Applications, 2019(118): 272-299.
|
[11] |
Jiang N, Tian F, Li J, et al. MAN: mutual attention neural networks model for aspect-level sentiment classification in siot[J]. IEEE Internet of Things Journal, 2020(1): 1.
|
[12] |
Tang D, Qin B, Liu T. Aspect level sentiment classification with deep memory network [C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2016: 214-224.
|
[13] |
Yin Y, Song Y, Zhang M. Document-level multi-aspect sentiment classification as machine comprehension [C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2017: 2044-2054.
|
[14] |
Yang C, Kim T, Wang R Z, et al. Show, attend, and translate: unsupervised image translation with self-regularization and attention[J]. IEEE Transactions on Image Processing, 2019, 10(28): 4845-4856.
|
[15] |
Wang Y, Huang M, Zhu X, et al. Attention-based LSTM for aspect-level sentiment classification [C]// Empirical Methods in Natural Language Processing. 2016: 606-615.
|
[16] |
Tay Y, Tuan L A, Hui S C. Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis [C]// Proceedings of the Thirty-Second Aaai Conference on Artificial Intelligence (AAAI). 2018: 5956-5963.
|
[17] |
Ma D, Li S, Zhang X, et al. Interactive attention networks for aspect-level sentiment classification [C]// Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI). 2017: 4069-4074.
|
[18] |
Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM [C]// Proceedings of the Thirty-Second AAAI Conference On Artificial Intelligence (AAAI). 2018: 5876-5883.
|
[19] |
Zhuang L, Schouten K, Frasincar F. SOBA: semi-automated ontology builder for aspect-based sentiment analysis[J]. Journal of Web Semantics, 2020, 60: 100-144.
|
[20] |
Baas F, Bus O, Osinga A, et al. Exploring lexico-semantic patterns for aspect-based sentiment analysis [C]// Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019: 984-992.
|
[21] |
Khine W L K, Nyein T T A. Applying deep learning approach to targeted aspect-based sentiment analysis for restaurant domain [C]// 2019 International Conference on Advanced Information Technologies (ICAIT). 2019: 206-211.
|
[22] |
Weston J, Chopra S, Bordes A. Memory networks [C]// International Conference on Learning Representations (ICLR). 2015: 2440-2448.
|
[23] |
Sukhbaatar S, Szlam A, Weston J, et al. End-to-end memory networks [C]// Neural Information Processing Systems. 2015: 2440-2448.
|
[24] |
Tang D, Qin B, Liu T, et al. Aspect level sentiment classification with deep memorynetwork [C]// Empirical Methods In Natural Language Processing. 2016: 214-224.
|
[25] |
Chen P, Sun Z, Bing L, et al. Recurrent attention network on memory for aspect sentiment analysis [C]// Empirical Methods In Natural Language Processing. 2017: 452-461.
|
[26] |
Pennington J, Socher R, Manning C D, et al. Glove: global vectors for word represen-tation [C]// Proceeding of the 2014 Empirical Methods in Natural Language Processing. 2014: 1532-1543.
|
[27] |
Wang S, Wang J, Wang Z, et al. Multiple emotion tagging for multimedia data by exploiting high-order dependencies among emotions[J]. IEEE Transactions on Multimedia, 2015, 17(12): 2185-2197.
doi: 10.1109/TMM.2015.2484966
|
[28] |
Kingma D P, Ba J. Adam: A method for stochastic optimization [C]// 3rd International Conference on Learning Representations. 2015: 7-9.
|
[29] |
Yang C, Zhang H, Jiang B, et al. Aspect-based sentiment analysis with alternating co-attention networks[J]. Information Processing and Management, 2019, 56(3): 463-478.
doi: 10.1016/j.ipm.2018.12.004
|
[30] |
Ma D, Li S, Zhang X, et al. Interactive attention networks for aspect-level sentiment classification [C]// International Joint Conference on Artificial Intelligence. 2017: 4068-4074.
|
[31] |
Fan F, Feng Y, Zhao D, et al. Multi-grained attention network for aspect-level sentiment classification [C]// Empirical Methods in Natural Language Processing. 2018: 3433-3442.
|
[32] |
Xu Q, Zhu L, Dai T, et al. Aspect-based sentiment classification with multi-attentionnetwork [EB/OL]. [2020-12-30]. https://xueshu.baidu.com/usercenter/paper/show?paperid=1q100emox06qom901j3jov30ca396844&site=xueshu_se.
|
[33] |
Wu S, Xu Y, Wu F, et al. Aspect-based sentiment analysis via fusing multiple sources of textual knowledge[J]. Knowledge Based Systems, 2019, 183(11): 1-16.
|