Abstract
Aspect-level sentiment classification is a fine-grained task that provides more complete and deeper analysis results. Attention networks are widely used for aspect-level sentiment classification task. However, when multiple target words in a sentence contain opposite sentiments or the expressions of different targets are similar, the network tends to perform poorly. Our studies find that the method of averaging a sentence or target words weakens the capacity of key words. A target information fusion memory network is proposed to solve this problem in this paper. Firstly, the feature of sentences is extracted through a Bi-LSTM network. Then, the feature of target is extracted incorporated into the sentence feature extracted. Then, the memory information of the specific target is formed by the position coding. Finally, the recurrent attention network is utilized to extract the sentiment expression from the memory. Compare with IAN, the method proposed achieves 1.5% and 1.9% accuracy improvement on SemEvil2014 restaurant dataset and self-defined Chinese mobile phone dataset, respectively. A further extend experiment proves that the proposed method can effectively improve the performance in the case of complex sentences.
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
References
Zhang, L., Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 30(1), 1–167 (2016)
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. (2018)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240 (2008)
Perez-Rosas, V., Banea, C., Mihalcea, R.: Learning sentiment lexicons in Spanish. European Language Resources Association (ELRA) (2012)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. Comput. Sci. (2015)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2017)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Conference on Empirical Methods in Natural Language Processing, pp. 214–224 (2016)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 4068–4074 (2017)
Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017)
Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification, pp. 946–956. Association for Computational Linguistics (2018)
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Exploiting document knowledge for aspect-level sentiment classification, pp. 579–585. Association for Computational Linguistics (2018)
Hazarika, D., Poria, S., Vij, P., Krishnamurthy, G., Cambria, E., Zimmermann, R.: Modeling inter-aspect dependencies for aspect-based sentiment analysis. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 266–270 (2018)
Pennington, J., Socher, R., Manning, C.: Glove: Global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: International Conference on Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc. (2013)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Acknowledgements
Our work is supported by Key Research and Development Projects of Xinjiang Autonomous Region in 2018 (No. 2018B03022-1 and No. 2018B03022-2), Innovation Project of GUET Graduate Education (No. 2018YJCX38 and No. 2017YJCX38) and Key Research and Guangxi Director Fund of the Key Laboratory of Wireless Broadband Communication and Signal Processing (No. GXKL0614107).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, Z., Peng, J., Cai, X., He, G. (2020). Target Information Fusion Based on Memory Network for Aspect-Level Sentiment Classification. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-16946-6_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16945-9
Online ISBN: 978-3-030-16946-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)