Abstract
Opinion summarization is based on aspect analyses of products, events or topics, which is a very interesting topic in natural language processing. Opinions are often expressed in various different ways in regards to objects. Therefore, it is important to express the characteristics of a product, event or topic in a final summary compiled by an automatic summarizing system. This paper proposes a method for conducting data preprocessing on the sentence level of a text using Convolutional Neural Networks. The corpus includes Vietnamese opinions on cars collected from social networking sites, forums, online newspapers and the websites of automobile dealers. The data processing phase will standardize terms for aspects that occur in opinion expressing aspects of the product. These aspects are used by manufacturers. Similarly, the standardization will be performed for both positive and negative terms used in opinions. The sentiment terms in the opinions will be replaced by standardized sentiment terms expressing the same sentiment polarities as those being replaced. This standardization is performed with the support of a semantic and sentiment ontology which has a tree hierarchy in the case of cars. This ontology ensures that the semantics and sentiment of the original opinion are not changed. The experimental results of the paper show that the proposed method gives better results than using no data preprocessing method for deep learning.
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
Li, Q., Jin, Z., Wang, C., Zeng, D.D.: Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems. Knowl.-Based Syst. 107, 289–300 (2016)
Wu, H., Gu, Y., Sun, S., Gu, X.: Aspect-based opinion summarization with convolutional neural networks. In: International Joint Conference Neural Networks, IJCNN, pp. 3157–3163. IEEE (2016)
Dhanush, D., Thakur, A.K., Diwakar, N.P.: Aspect-based sentiment summarization with deep neural networks. Int. J. Eng. Res. Technol. 5(5), 371–375 (2016)
Thien, K.T., Tuoi, T.P.: Computing sentiment scores of verb phrases for Vietnamese. In: Proceedings of the Conference on Computational Linguistics and Speech Processing, pp. 204–213. ROCLING (2016)
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Proceedings of the Workshop on Text Summarization Branches Out, pp. 74–81 (2004)
Xu, L., Lin, J., Wang, L., Yin, C., Wang, J.: Deep convolutional neural network based approach for aspect-based sentiment analysis. Adv. Sci. Technol. Lett. 143, 199–204 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1746–1751 (2014)
Hu, M., Liu, B.: Mining opinion features in customer reviews. Assoc. Adv. Artif. Intell. 4(4), 755–760 (2004)
Liang, L.C., Hoar, H.W., Hoang, L.C., Chi, L.G., Emery, J.: Movie rating and review summarization in mobile environment. IEEE Trans. Syst. Man Cybern.-Part C: Appl. Rev. 42, 397–406 (2012)
Xin, R.: word2vec parameter learning explained, in arXiv preprint arXiv:1411.2738 (2016)
Liao, C., Wang, J., Yu, R., Sato, K., Cheng, Z.: CNN for situations understanding based on sentiment analysis of Twitter data. Procedia Comput. Sci. 111, 376–381 (2017)
Acknowledgments
This paper was supported by research project TNCS_KHMT_2017_06, funded by the Ho Chi Minh City University of Technology, VNU-HCM.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen Ngoc, D., Phan Thi, T., Do, P. (2019). A Data Preprocessing Method to Classify and Summarize Aspect-Based Opinions Using Deep Learning. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-14799-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14798-3
Online ISBN: 978-3-030-14799-0
eBook Packages: Computer ScienceComputer Science (R0)