Abstract
Aspect-based sentiment analysis (ABSA) refers to classifying the sentiment polarity of a specific aspect in a sentence. Recently, attention-based deep learning approaches are proposed to capture the semantic information and achieve satisfying results. However, due to the significance of syntactic structure, syntactic information is also analyzed for ABSA. As such, this work proposes a model that integrates the graph convolution network (GCN) and the co-attention mechanism to deal with the aspect-based information and remove the noise from unrelated context words. Both the semantic information and the syntactic information are conveyed by the representation for sentiment analysis. Experimental results show our model achieves a better working performance, which establishes a strong evidence of the capability.
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References
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Xianghua, F., Yang, J., Li, J., Fang, M., Wang, H.: Lexicon-enhanced LSTM with attention for general sentiment analysis. IEEE Access 6, 71884–71891 (2018)
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
Zeng, J., Ma, X., Zhou, K.: Enhancing attention-based LSTM with position context for aspect-level sentiment classification. IEEE Access 7, 20462–20471 (2019)
Minh, D.L., Sadeghi-Niaraki, A., Huy, H.D., Min, K., Moon, H.: Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access 6, 55392–55404 (2018)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)
Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019)
Ma, X., Zeng, J., Peng, L., Fortino, G., Zhang, Y.: Modeling multi-aspects within one opinionated sentence simultaneously for aspect-level sentiment analysis. Futur. Gener. Comput. Syst. 93, 304–311 (2019)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893 (2017)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Sima’an, K.: Graph convolutional encoders for syntax-aware neural machine translation. arXiv preprint arXiv:1704.04675 (2017)
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: ACL (vol. 2: Short papers), pp. 49–54 (2014)
Maria Pontiki, D.G., John Pavlopoulos, H.P., Ion Androutsopoulos, S.M.: Semeval-2014 task 4: Semeval-2014 task 4: aspect based sentiment analysis. In: SemEval 2014, Dublin, Ireland, pp. 23–24 (2014)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: SemEval 2015, pp. 486–495 (2015)
Pontiki, M., et al.: Semeval- 2016 task 5: aspect based sentiment analysis. In: International Workshop on Semantic Evaluation, pp. 19–30 (2016)
Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) SBP-BRiMS 2018. LNCS, vol. 10899, pp. 197–206. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93372-6_22
Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. arXiv preprint arXiv:1805.01086 (2018)
Yuan, L., Wang, J., Yu, L.C., Zhang, X.: Graph attention network with memory fusion for aspect-level sentiment analysis. In: AACL and the IJCNLP, pp. 27–36 (2020)
Zhang, M., Qian, T.: Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: EMNLP, pp. 3540–3549 (2020)
Acknowledgments
This work was supported by Humanity and Social Science Foundation of the Ministry of Education of China (21A13022003), Zhejiang Provincial Natural Science Fund (LY19F030010), Zhejiang Provincial Social Science Fund (20NDJC216YB), Zhejiang Educational Science Fund (GH2021642), the Science and Technology Plan Project of Guangzhou under Grant Nos. 202102080258 and 201903010013.
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Chen, Z., Xue, Y., Xiao, L., Chen, J., Zhang, H. (2021). Aspect-Based Sentiment Analysis Using Graph Convolutional Networks and Co-attention Mechanism. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_51
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