Abstract
Aspect-level sentiment classification aims to predict the sentiment polarity towards the given aspects of sentences. Neural network models with attention mechanism have achieved great success in this area. However, existing methods fail to capture enough aspect information. Besides, it is hard for simple attention mechanism to model complex interaction between aspects and contexts. In this paper, we propose a Segment Model with Dual Attention (SegM-DA) to tackle these problems. We combine deep learning models with traditional methods by defining dependency rules to extract auxiliary words, which helps to enrich aspect information. In addition, in order to model structural relation between aspects and contexts, we introduce dependent attention mechanism. Coupled with standard attention mechanism, we establish the dual attention mechanism, which models the interaction from both word- and structure- dependency. We perform aspect-level sentiment classification experiments on two real datasets. The results show that our model can achieve the state-of-the-art performance.
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Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: EMNLP, pp. 452–461 (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 (volume 2: Short papers), vol. 2, pp. 49–54 (2014)
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Effective attention modeling for aspect-level sentiment classification. In: COLING, pp. 1121–1131 (2018)
Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. arXiv preprint arXiv:1805.01086 (2018)
Liu, J., Zhang, Y.: Attention modeling for targeted sentiment. In: EACL, vol. 2, Short Papers, pp. 572–577 (2017)
Liu, Q., Zhang, H., Zeng, Y., Huang, Z., Wu, Z.: Content attention model for aspect based sentiment analysis. In: WWW, 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)
Nandan, N., Dahlmeier, D., Vij, A., Malhotra, N.: SAP-RI: a constrained and supervised approach for aspect-based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 517–521 (2014)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Piryani, R., Gupta, V., Singh, V.K., Ghose, U.: A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds.) Advances in Computer and Computational Sciences. AISC, vol. 553, pp. 201–209. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-3770-2_19
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: SemEval 2014, pp. 27–35. ACL, Aug 2014
Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L.: Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 41–47. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12024-9_5
Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: IJCAI (2009)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
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)
Vo, D.T., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. In: IJCAI (2015)
Wang, B., Lu, W.: Learning latent opinions for aspect-level sentiment classification. In: AAAI (2018)
Wang, Y., Huang, M., Zhao, L., et al.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)
Zhu, P., Qian, T.: Enhanced aspect level sentiment classification with auxiliary memory. In: COLING, pp. 1077–1087 (2018)
Acknowledgment
The work described in this paper has been supported in part by the NSFC project (61572376).
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Yang, Y., Qian, T., Chen, Z. (2019). Aspect-Level Sentiment Classification with Dependency Rules and Dual Attention. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_54
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