[1]
|
Xue, W. and Li, T. (2018) Aspect Based Sentiment Analysis with Gated Convolutional Networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 1, 2514-2523.
https://doi.org/10.18653/v1/P18-1234
|
[2]
|
何炎祥, 孙松涛, 牛菲菲, 等. 用于微博情感分析的一种情感语义增强的深度学习模型[J]. 计算机学报, 2017, 40(4): 773-790.
|
[3]
|
Zhang, L., Wang, S. and Liu, B. (2018) Deep Learning for Sentiment Analysis: A Survey. WIREs Data Mining and Knowledge Discovery, 8, e1253. https://doi.org/10.1002/widm.1253
|
[4]
|
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444.
https://doi.org/10.1038/nature14539
|
[5]
|
Socher, R., Perelygin, A., Wu, J., et al. (2013) Recursive Deep Models for Semantic Compositionality over a Sentiment Treebank. Empirical Methods in Natural Language Processing, 1631-1642.
|
[6]
|
Kim, Y. (2014) Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, October 2014, 1746-1751.
https://doi.org/10.3115/v1/D14-1181
|
[7]
|
Wang, X., Liu, Y., Sun, C., et al. (2015) Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory. Proceedings of the 53rd Annual Meeting of the Asso-ciation for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1, 1343-1353.
https://doi.org/10.3115/v1/P15-1130
|
[8]
|
Mnih, V., Heess, N., Graves, A., et al. (2014) Recurrent Models of Vis-ual Attention. arXiv: Learning.
|
[9]
|
Zhang, L. and Liu, B. (2014) Aspect and Entity Extraction for Opinion Mining. In: Chu, W., Ed., Data Mining and Knowledge Discovery for Big Data. Studies in Big Data, Vol. 1. Springer, Berlin, Hei-delberg.
https://doi.org/10.1007/978-3-642-40837-3_1
|
[10]
|
Mimno, D., Wallach, H., Talley, E.M., et al. (2011) Optimizing Semantic Coherence in Topic Models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Proceedings, Edinburgh, 27-31 July, 262-272.
|
[11]
|
Yan, X., Guo, J., Lan, Y., et al. (2013) A Biterm Topic Model for Short Texts. Proceedings of the 22nd International Conference on World Wide Web, May 2013, 1445-1456. https://doi.org/10.1145/2488388.2488514
|
[12]
|
Wang, L., Liu, K., Cao, Z., et al. (2015) Sentiment-Aspect Extrac-tion Based on Restricted Boltzmann Machines. Proceedings of the 53rd Annual Meeting of the Association for Computa-tional Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, 26-31 July, 616-625.
|
[13]
|
He, R., Lee, W.S., Ng, H.T., et al. (2017) An Unsupervised Neural Attention Model for Aspect Extrac-tion. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 1, 388-397.
https://doi.org/10.18653/v1/P17-1036
|
[14]
|
Wilson, T., Wiebe, J. and Hoffmann, P. (2015) Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. International Journal of Computer Applications, 7, 347-354.
|
[15]
|
Wu, Y., Wang, M. and Jin, P. (2008) Disambiguating Sentiment Ambiguous Adjectives. International Conference on Natural Language Processing and Knowledge Engineering, Beijing, 19-22 October 2008, 1-8.
https://doi.org/10.1109/NLPKE.2008.4906816
|
[16]
|
Tang, D., Qin, B., Feng, X., et al. (2015) Effective LSTMs for Target-Dependent Sentiment Classification. Proceedings of COLING 2016, the 26th International Conference on Com-putational Linguistics: Technical Paper, Osaka, 11-17 December 2017, 3298-3307.
|
[17]
|
Wang, Y., Huang, M., Zhu, X., et al. (2016) Attention-Based LSTM for Aspect-Level Sentiment Classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, November 2016, 606-615. https://doi.org/10.18653/v1/D16-1058
|
[18]
|
Liu, J. and Zhang, Y. (2017) Attention Modeling for Targeted Senti-ment. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, April 2017, 572-577.
https://doi.org/10.18653/v1/E17-2091
|
[19]
|
Tang, D., Qin, B. and Liu, T. (2016) Aspect Level Sentiment Classifica-tion with Deep Memory Network. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, November 2016, 214-224.
https://doi.org/10.18653/v1/D16-1021
|
[20]
|
Ma, D., Li, S., Zhang, X., et al. (2017) Interactive Attention Networks for Aspect-Level Sentiment Classification. Proceedings of the 26th International Joint Conference on Artificial Intelli-gence, 4068-4074.
https://doi.org/10.24963/ijcai.2017/568
|
[21]
|
Chen, P., Sun, Z., Bing, L., et al. (2017) Recurrent Attention Network on Memory for Aspect Sentiment Analysis. Proceedings of the 2017 Conference on Empirical Methods in Natural Lan-guage Processing, Copenhagen, September 2017, 452-461. https://doi.org/10.18653/v1/D17-1047
|
[22]
|
Li, X., Bing, L., Lam, W., et al. (2018) Transformation Networks for Target-Oriented Sentiment Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 1, 946-956.
https://doi.org/10.18653/v1/P18-1087
|
[23]
|
Tang, J., Lu, Z., Su, J., et al. (2019) Progressive Self-Supervised Atten-tion Learning for Aspect-Level Sentiment Analysis. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, July 2019, 557-566. https://doi.org/10.18653/v1/P19-1053
|
[24]
|
Chen, Z., Mukherjee, A., Liu, B., et al. (2014) Aspect Extraction with Automated Prior Knowledge Learning. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, 347-358.
https://doi.org/10.3115/v1/P14-1033
|
[25]
|
Ren, S., He, K., Girshick, R., et al. (2015) Faster R-CNN: Towards Re-al-Time Object Detection with Region Proposal Networks. Neural Information Processing Systems, 91-99.
|