Abstract
Aspect-level sentiment analysis is a fine-grained task in sentiment analysis, whose target is to identify the sentiment polarity of a specific aspect in a sentence. Due to the complexity of the human language, the widely-applied syntactic-based neural network methods have deficiencies in precisely capturing the relation between aspects and opinion words, and thus results in the misunderstanding of the sentiment. To address such issue, we focus on optimizing the encoding of syntactic information. To start with, the sub-dependency trees, from the basic dependency tree, are constructed in line with the syntactic distance. Further, we propose a novel Highway-Based Local Graph Convolution Network (HL-GCN) to capture the more-related information and thus facilitate the sentiment classification. Substantial experiments on a variety of datasets are performed. Comparing to the state-of-arts, the proposed model shows the effectiveness in eliminating the noise from the dependency tree, which results in an even higher classification accuracy.
S. Pang and Z. Yan—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Data and code can be found at https://github.com/pangsg/HLGCN.
References
Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: EMNLP, pp. 1035–1045 (2010)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)
Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: EMNLP, pp. 452–461 (2017)
Li, H., Xue, Y., Zhao, H., Hu, X., Peng, S.: Co-attention networks for aspect-level sentiment analysis. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11839, pp. 200–209. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32236-6_17
Lakkaraju, H., Socher, R., Manning, C.: Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS Workshop on deep learning and representation learning, pp. 1–9 (2014)
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: ACL, pp. 49–54 (2014)
Nguyen, T.H., Shirai, K.: PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis. In: EMNLP, pp. 2509–2514 (2015)
Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Recursive neural conditional random fields for aspect-based sentiment analysis. In: EMNLP, pp. 616–626 (2016)
Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019)
Zhang, C., Li, Q., Song, D.: Syntax-aware aspect-level sentiment classification with proximity-weighted convolution network. In: ACM SIGIR, pp. 1145–1148 (2019)
Sun, K., Zhang, R., Mensah, S., Mao, Y., Liu, X.: Aspect-level sentiment analysis via convolution over dependency tree. In: (EMNLP-IJCNLP), pp. 5683–5692 (2019)
Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: ACL-IJCNLP (2020)
Tang, H., Ji, D., Li, C., Zhou, Q.: Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: ACL, pp. 6578–6588 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Wang, S., Mazumder, S., Liu, B., Zhou, M., Chang, Y.: Target-sensitive memory networks for aspect sentiment classification. In: ACL, pp. 957–967 (2018)
Li, L., Liu, Y., Zhou, A.: Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: CoNLL, pp. 181–189 (2018)
Zeng, J., Ma, X., Zhou, K.: Enhancing attention-based LSTM with position context for aspect-level sentiment classification. IEEE Access 7, 20462–20471 (2019)
Xu, H., Liu, B., Shu, L., Yu, P.S.: Bert post-training for review reading comprehension and aspect-based sentiment analysis. arXiv preprint arXiv:1904.02232 (2019)
Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)
Pontiki, M., Papageorgiou, H., Galanis, D., Androutsopoulos, I., Pavlopoulos, J., Manandhar, S.: Semeval-2014 task 4: Aspect based sentiment analysis. In: SemEval, vol. 2014, p. 27 (2014)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Sun, K., Zhang, R., Mensah, S., Mao, Y., Liu, X.: Aspect-level sentiment analysis via convolution over dependency tree. In: EMNLP-IJCNLP, pp. 5683–5692 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Pang, S., Yan, Z., Huang, W., Tang, B., Dai, A., Xue, Y. (2021). Highway-Based Local Graph Convolution Network for Aspect Based Sentiment Analysis. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-88480-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88479-6
Online ISBN: 978-3-030-88480-2
eBook Packages: Computer ScienceComputer Science (R0)
