Aspect Aware Learning for Aspect Category Sentiment Analysis

Abstract

Aspect category sentiment analysis (ACSA) is an underexploited subtask in aspect level sentiment analysis. It aims to identify the sentiment of predefined aspect categories. The main challenge in ACSA comes from the fact that the aspect category may not occur in the sentence in most of the cases. For example, the review “they have delicious sandwiches” positively talks about the aspect category “food” in an implicit manner.

In this article, we propose a novel aspect aware learning (AAL) framework for ACSA tasks. Our key idea is to exploit the interaction between the aspect category and the contents under the guidance of both sentiment polarity and predefined categories. To this end, we design a two-way memory network for integrating AAL into the framework of sentiment classification. We further present two algorithms to incorporate the potential impacts of aspect categories. One is to capture the correlations between aspect terms and the aspect category like “sandwiches” and “food.” The other is to recognize the aspect category for sentiment representations like “food” for “delicious.” We conduct extensive experiments on four SemEval datasets. The results reveal the essential role of AAL in ACSA by achieving the state-of-the-art performance.

References

1. Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Conference on Empirical Methods in Natural Language Processing (EMNLP’17). 452--461.
2. Kenneth Ward Church and Patrick Hanks. 1990. Word association norms, mutual information, and lexicography. Computational Linguistics 16, 1(1990), 22--29.
3. 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 Annual Meeting of the Association for Computational Linguistics (ACL’14). 49--54.
4. Jiachen Du, Ruifeng Xu, Yulan He, and Lin Gui. 2017. Stance classification with target-specific neural attention. In International Joint Conferences on Artificial Intelligence (IJCAI’17). 3988--3994.
5. W. L. Hamilton, K. Clark, J. Leskovec, and D. Jurafsky. 2016. Inducing domain-specific sentiment lexicons from unlabeled corpora. In Conference on Empirical Methods in Natural Language Processing (EMNLP’16). 595--605.
6. Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2017. An unsupervised neural attention model for aspect extraction. In Annual Meeting of the Association for Computational Linguistics (ACL’17). 388--397.
7. Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Exploiting document knowledge for aspect-level sentiment classification. In Annual Meeting of the Association for Computational Linguistics (ACL’18). 579--585.
8. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780.
9. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
10. Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. Target-dependent twitter sentiment classification. In Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 151--160.
11. Soo Min Kim and E. H. Crystal Hovy. 2007. Analyzing predictive opinions on the web. In Conference on Empirical Methods on Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’07). 1056--1064.
12. O. Levy, Y. Goldberg, and I. Dagan. 2015. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics 3 (2015), 211--225.
13. Cheng Li, Xiaoxiao Guo, and Qiaozhu Mei. 2017. Deep memory networks for attitude identification. In 10th ACM International Conference on Web Search and Data Mining (WSDM’17). 671--680.
14. Xin Li, Lidong Bing, Wai Lam, and Bei Shi. 2018. Transformation networks for target-oriented sentiment classification. In Annual Meeting of the Association for Computational Linguistics (ACL’18). 946--956.
15. Xin Li and Wai Lam. 2017. Deep multi-task learning for aspect term extraction with memory interaction. In Conference on Empirical Methods in Natural Language Processing (EMNLP’17). 2886--2892.
16. Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Springer, Berlin.
17. Dehong Ma, Sujian Li, Xiaodong Zhang, and Houfeng Wang. 2017. Interactive attention networks for aspect-level sentiment classification. In International Joint Conferences on Artificial Intelligence (IJCAI’17). 4068--4074.
18. Yukun Ma, Haiyun Peng, and Erik Cambria. 2018. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In AAAI Conference on Artificial Intelligence.
19. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. ICLR (Workshop Poster).
20. Saif M. Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. 2013. NRC-canada: Building the state-of-the-art in sentiment analysis of tweets. In International Workshop on Semantic Evaluation (SemEval’13).
21. Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In Annual Conference of the North American Chapter of the Association for Computational Linguistics. 786--794.
22. Tetsuya Nasukawa and Jeonghee Yi. 2003. Sentiment analysis: Capturing favorability using natural language processing. In 2nd international conference on Knowledge capture (K-CAP’03). 70--77.
23. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Conference on Empirical Methods in Natural Language Processing (EMNLP’02). 79--86.
24. Minlong Peng, Qi Zhang, Yugang Jiang, and Xuanjing Huang. 2018. Cross-domain sentiment classification with target domain specific information. In Annual Meeting of the Association for Computational Linguistics (ACL’18). 2505--2513.
25. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1532--1543.
26. Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia V. Loukachevitch, Evgeniy V. Kotelnikov, Núria Bel, Salud María Jiménez Zafra, and Gülsen Eryigit. 2016. SemEval-2016 task 5: Aspect based sentiment analysis. In International Workshop on Semantic Evaluation (SemEval’16). 19--30.
27. Maria Pontiki, Dimitrios Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. 2015. SemEval-2015 task 12: Aspect based sentiment analysis. In International Workshop on Semantic Evaluation (SemEval’15). 486--495.
28. Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. SemEval-2014 Task 4: Aspect based sentiment analysis. International Workshop on Semantic Evaluation (SemEval’14). 27--35.
29. Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. 2015. End-to-end memory networks. In Advances in Neural Information Processing Systems (NIPS’15). 2440--2448.
30. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods for sentiment analysis. Computational Linguistics 37, 2 (2011), 267--307.
31. Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. 2016. Effective LSTMs for target-dependent sentiment classification. In International Conference on Computational Linguistics (COLING’16). 3298--3307.
32. Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect level sentiment classification with deep memory network. In Conference on Empirical Methods in Natural Language Processing (EMNLP’16). 214--224.
33. Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2017. Dyadic memory networks for aspect-based sentiment analysis. In ACM International Conference on Information and Knowledge Management (CIKM’17). 107--116.
34. Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In AAAI Conference on Artificial Intelligence.
35. Duy-Tin Vo and Yue Zhang. 2015. Target dependent twitter sentiment classification with rich automatic features. In International Joint Conferences on Artificial Intelligence (IJCAI’15). 1347--1353.
36. Leyi Wang and Rui Xia. 2017. Sentiment lexicon construction with representation learning based on hierarchical sentiment supervision. In Conference on Empirical Methods in Natural Language Processing (EMNLP’17). 502--510.
37. Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, and Yi Chang. 2018. Target-sensitive memory networks for aspect sentiment classification. In Annual Meeting of the Association for Computational Linguistics (ACL’18). 957--967.
38. Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2016. Recursive neural conditional random fields for aspect-based sentiment analysis. In Conference on Empirical Methods in Natural Language Processing (EMNLP’16). 616--626.
39. Yequan Wang, Minlie Huang, Li Zhao, and Xiaoyan Zhu. 2016. Attention-based LSTM for aspect-level sentiment classification. In Conference on Empirical Methods in Natural Language Processing (EMNLP’16). 606--615.
40. Wei Xue and Tao Li. 2018. Aspect based sentiment analysis with gated convolutional networks. In Annual Meeting of the Association for Computational Linguistics (ACL’18). 2514--2523.
41. Jun Yang, Runqi Yang, Chong-Jun Wang, and Jun-Yuan Xie. 2018. Multi-entity aspect-based sentiment analysis with context, entity and aspect memory. In AAAI Conference on Artificial Intelligence.
42. Peisong Zhu and Tieyun Qian. 2018. Enhanced aspect level sentiment classification with auxiliary memory. In International Conference on Computational Linguistics (COLING’18). 1077--1087.

Index Terms

• Information systems

  • Information systems applications

    • Data mining