Recurrent attention network on memory for aspect sentiment analysis

EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Volumn , Issue , 2017, Pages 452-461

  Chen, Peng ^a   Sun, Zhongqian ^a   Bing, Lidong ^a   Yang, Wei ^a  

^a TENCENT AI LAB   (China)

Author keywords

[No Author keywords available]

Indexed keywords

SENTIMENT ANALYSIS; SOCIAL NETWORKING (ONLINE); STATISTICAL TESTS;

ATTENTION MECHANISMS; EXPRESSIVE POWER; FEATURE ENGINEERINGS; LABOR INTENSIVE; MEMORY MECHANISM; SENTIMENT FEATURES; SOCIAL MEDIA DATUM; STATE-OF-THE-ART METHODS;

RECURRENT NEURAL NETWORKS;

EID: 85073152063     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d17-1047     Document Type: Conference Paper

References (25)

1. Orestes Appel, Francisco Chiclana, Jenny Carter, and Hamido Fujita. 2016. A hybrid approach to the sentiment analysis problem at the sentence level. Knowl.-Based Syst., 108:110–124.
2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473.
3. Peng Chen, Bing Xu, Muyun Yang, and Sheng Li. 2016. Clause sentiment identification based on convolutional neural network with context embedding. In Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016 12th International Conference on, pages 1532–1538.
4. KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. CoRR, abs/1409.1259.
5. Lingjia Deng and Janyce Wiebe. 2015. Joint prediction for entity/event-level sentiment analysis using probabilistic soft logic models. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.
6. Xiaowen Ding, Bing Liu, and Philip S Yu. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 international conference on web search and data mining, pages 231–240.
7. Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).
8. Murthy Ganapathibhotla and Bing Liu. 2008. Mining opinions in comparative sentences. In Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pages 241–248.
9. Alex Graves, Greg Wayne, and Ivo Danihelka. 2014. Neural turing machines. CoRR, abs/1410.5401.
10. Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. Target-dependent twitter sentiment classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 151–160.
11. Andrej Karpathy, Justin Johnson, and Fei-Fei Li. 2015. Visualizing and understanding recurrent networks. CoRR, abs/1506.02078.
12. Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, and Saif Mohammad. 2014. Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 437–442.
13. Ankit Kumar, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian Pierce, Peter Ondruska, Ishaan Gulrajani, and Richard Socher. 2015. Ask me anything: Dynamic memory networks for natural language processing. CoRR, abs/1506.07285.
14. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781.
15. Thien Hai Nguyen and Kiyoaki Shirai. 2015. Phrasernn: Phrase recursive neural network for aspect-based sentiment analysis. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.
16. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543.
17. Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. Semeval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pages 27–35.
18. Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. CoRR, abs/1509.00685.
19. Richard Socher, Christopher D Manning, and Andrew Y Ng. 2010. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop, pages 1–9.
20. Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pages 151–161.
21. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment tree-bank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.
22. Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. 2015. Target-dependent sentiment classification with long short term memory. CoRR, abs/1512.01100.
23. Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect level sentiment classification with deep memory network. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.
24. Yequan Wang, Minlie Huang, xiaoyan zhu, and Li Zhao. 2016. Attention-based lstm for aspect-level sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.
25. Wayne Xin Zhao, Jing Jiang, Hongfei Yan, and Xiaoming Li. 2010. Jointly modeling aspects and opinions with a maxent-lda hybrid. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP’10, pages 56–65.