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Aspect-Level Sentiment Classification with Dependency Rules and Dual Attention

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 11954))

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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 aSegmentModel withDualAttention (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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References

  1. Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: EMNLP, pp. 452–461 (2017)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Effective attention modeling for aspect-level sentiment classification. In: COLING, pp. 1121–1131 (2018)

    Google Scholar 

  4. Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. arXiv preprintarXiv:1805.01086 (2018)

  5. Liu, J., Zhang, Y.: Attention modeling for targeted sentiment. In: EACL, vol. 2, Short Papers, pp. 572–577 (2017)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv preprintarXiv:1709.00893 (2017)

  8. 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)

    Google Scholar 

  9. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

    Google Scholar 

  10. 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

    Chapter  Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Chapter  Google Scholar 

  13. Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: IJCAI (2009)

    Google Scholar 

  14. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist.37(2), 267–307 (2011)

    Article  Google Scholar 

  15. Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. arXiv preprintarXiv:1512.01100 (2015)

  16. Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv preprintarXiv:1605.08900 (2016)

  17. Vo, D.T., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. In: IJCAI (2015)

    Google Scholar 

  18. Wang, B., Lu, W.: Learning latent opinions for aspect-level sentiment classification. In: AAAI (2018)

    Google Scholar 

  19. Wang, Y., Huang, M., Zhao, L., et al.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)

    Google Scholar 

  20. Zhu, P., Qian, T.: Enhanced aspect level sentiment classification with auxiliary memory. In: COLING, pp. 1077–1087 (2018)

    Google Scholar 

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Acknowledgment

The work described in this paper has been supported in part by the NSFC project (61572376).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, Hubei, China

    Yunkai Yang, Tieyun Qian & Zhuang Chen

Authors
  1. Yunkai Yang

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  2. Tieyun Qian

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  3. Zhuang Chen

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Corresponding author

Correspondence toTieyun Qian.

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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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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