Abstract
We present a hybrid method for aspect-based sentiment analysis of Chinese restaurant reviews. Two main components are employed so as to extract feature-opinion pairs in the proposed method: domain independent language patterns found in Chinese and a lexical base built for restaurant reviews. The language patterns focus on the general knowledge which is implicit contained in Chinese, thus can be used directly by other domains without any modification. The lexical base, on the other hand, targets for particular characteristics of a given domain and acts as a plug-in part in our prototype system, thus does not affect the portability when applying the proposed approach in practice. Empirical evaluation shows that our method performs well and it can gain a progressive result when each component takes into effective.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Proceeding of HLT 2010, pp. 804–812 (2010)
Bross, J., Ehrig, H.: Automatic construction of domain and aspect specific sentiment lexicons for customer review mining. In: Proceedings of CIKM 2013, pp. 1077–1086 (2013)
Freitas, L.A., Vieira, R.: Ontology based feature level opinion mining for portuguese reviews. In: Proceedings of WWW 2013 Companion, pp. 367–370 (2013)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of SIGKDD 2004, pp. 168–177 (2004)
Ittoo, A., Bouma, G.: Minimally-supervised learning of domain-specific causal relations using an open-domain corpus as knowledge base. Data & Knowledge Engineering 85, 57–79 (2013)
Jiao, F., Dong, G., Li, Q., Zhu, J.: Opinminer: Extracting feature-opinion pairs with dependency grammar from chinese product reviews. In: Proceedings of WISA 2012, pp. 217–222 (2012)
Li, F., Huang, M., Zhu, X.: Sentiment analysis with global topics and local dependency. In: Proceedings of AAAI 2010, pp. 1371–1376 (2010)
O’Leary, D.E.: Blog mining-review and extensions:“from each according to his opinion”. Decision Support Systems 51(4), 821–830 (2011)
Peñalver-Martínez, I., Valencia-García, R., García-Sánchez, F.: Ontology-guided approach to feature-based opinion mining. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 193–200. Springer, Heidelberg (2011)
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of HLT 2005, pp. 339–346 (2005)
Somprasertsri, G., Lalitrojwong, P.: Mining feature-opinion in online customer reviews for opinion summarization. Journal of Universal Computer Science 16(6), 938–955 (2010)
Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in chinese web opinion mining. In: Proceedings of WWW 2008, pp. 959–968 (2008)
Xu, X., Cheng, X., Tan, S., Liu, Y., Shen, H.: Aspect-level opinion mining of online customer reviews. Communications, China 10(3), 25–41 (2013)
Yin, P., Wang, H., Guo, K.: Feature pinion pair identification of product reviews in chinese: a domain ontology modeling method. New Review of Hypermedia and Multimedia 19(1), 3–24 (2013)
Zhai, Z., Liu, B., Zhang, L., Xu, H., Jia, P.: Identifying evaluative sentences in online discussions. In: Proceedings of AAAI 2011, pp. 933–938 (2011)
Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: Proceedings of CIKM 2006, pp. 43–50 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhou, E., Luo, X., Qin, Z. (2014). Incorporating Language Patterns and Domain Knowledge into Feature-Opinion Extraction. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2014. Lecture Notes in Computer Science(), vol 8655. Springer, Cham. https://doi.org/10.1007/978-3-319-10816-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-10816-2_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10815-5
Online ISBN: 978-3-319-10816-2
eBook Packages: Computer ScienceComputer Science (R0)