Abstract
Sentiment analysis is widely applied to online and offline applications such as marketing, customer service and social media. Aspect-based sentiment classification is a fine-grained sentiment analysis that identifies the sentiment polarity of a specific aspect in a given sentence. In order to model syntactical constraints and word dependencies in a sentence, graph convolutional network (GCN) has been introduced for aspect-based sentiment classification. Though achieved promising results, GCN becomes less effective when the aspect term is far from the key context words on the dependency tree. To tackle this problem, we propose a Multi-hop Syntactic Graph Convolutional Networks model, in which a syntactic graph convolutional network is constructed according to transmission way of information in the sentence structure. Then a multi-range attention mechanism is applied to deepen the number of layers of the model to aggregate further information on the dependency tree. Experiments on benchmarking collections show that our proposed model outperforms the state-of-the-art methods.
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Notes
- 1.
spaCy toolkit: https://spacy.io/.
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)
Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., Feng, X.: Multi-range attentive bicomponent graph convolutional network for traffic forecasting. arXiv preprint arXiv:1911.12093 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Huang, B., Carley, K.M.: Parameterized convolutional neural networks for aspect level sentiment classification. arXiv preprint arXiv:1909.06276 (2019)
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
Kiritchenko, S., Zhu, X., Cherry, C., Mohammad, S.: NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 437–442 (2014)
Li, L., Liu, Y., Zhou, A.: Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Proceedings of the 22nd Conference on Computational Natural Language Learning, pp. 181–189 (2018)
Liu, Q., Zhang, H., Zeng, Y., Huang, Z., Wu, Z.: Content attention model for aspect based sentiment analysis. In: Proceedings of the 2018 World Wide Web Conference, pp. 1023–1032. International World Wide Web Conferences Steering Committee (2018)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893 (2017)
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 (EMNLP), pp. 1532–1543 (2014)
Song, Y., Wang, J., Jiang, T., Liu, Z., Rao, Y.: Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314 (2019)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100 (2015)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900 (2016)
Wagner, J., et al.: DCU: Aspect-based polarity classification for SemEval task 4 (2014)
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. arXiv preprint arXiv:1805.07043 (2018)
Zhang, M., Zhang, Y., Vo, D.T.: Gated neural networks for targeted sentiment analysis. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. arXiv preprint arXiv:1809.10185 (2018)
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Yin, C., Zhou, Q., Ge, L., Ou, J. (2020). Multi-hop Syntactic Graph Convolutional Networks for Aspect-Based Sentiment Classification. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_20
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DOI: https://doi.org/10.1007/978-3-030-55393-7_20
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