Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis problem, which has attracted much attention in recent years. Previous methods mainly devote to employing attention mechanism to model the relationship between aspects and context words. However, these methods tend to ignore the overall semantics of sentence and dependency among the aspect terms. In this paper, we propose a Hierarchical Attention Network (HAN) to solve the aforementioned issues simultaneously. Experimental results on standard SemEval 2014 datasets demonstrate the effectiveness of the proposed model.
This paper is supported by the National Key Research and Development Program of China (Grant No. 2016YFB1001102), the National Natural Science Foundation of China (Grant Nos. 61876080, 61502227), the Fundamental Research Funds for the Central Universities No.020214380040, the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We run two LSTM forwards and backwards respectively and concatenate the two hidden vectors in i-th time step to produce \(h_i^a\).
- 2.
It is worth noting that RAM utilizes additional position information.
References
Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation, pp. 27–35 (2014)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016)
Wang, Y., Huang, M., Zhao, L., et al.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016)
Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 2514–2523 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ran, X., Pan, Y., Sun, W., Wang, C. (2019). Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_76
Download citation
DOI: https://doi.org/10.1007/978-3-030-18590-9_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18589-3
Online ISBN: 978-3-030-18590-9
eBook Packages: Computer ScienceComputer Science (R0)