Abstract
Aspect-based sentiment analysis is an emerging trend in the NLP area nowadays. One of the major tasks in this work is to identify corresponding aspects for rating sentiment. Ontology is considered highly useful to cope with this issue, due to its capability of capturing and representing concepts in a certain domain. However, ontology-based sentiment analysis suffers from the difficulty when handling anaphoric coreference of mentioned entities, which commonly occurs in textual documents. This paper addresses this problem by introducing an approach combining coreference resolution with ontology inference. The initial results are quite promising.
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
Similar content being viewed by others
Notes
References
Padmaja, S., Fatima, S.: Opinion mining and sentiment analysis - an assessment of peoples’ belief: a survey. Int. J. Ad hoc Sens. Ubiquitous Comput. 4(1), 21–33 (2013)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Kim, W., Kohavi, R., Gehrke, J., DuMouchel, W. (eds.) Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, 22–25 August 2004, pp. 168–177. ACM (2004)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60, June 2014
Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of Twitter posts. Expert Syst. Appl. 40(10), 4065–4074 (2013)
Lau, R.Y., Li, C., Liao, S.S.: Social analytics: learning fuzzy product ontologies for aspect-oriented sentiment analysis. Decis. Support Syst. 65, 80–94 (2014)
Thet, T.T., Na, J.C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. (2010). doi:10.1177/0165551510388123
Kim, S., Zhang, J., Chen, Z., Oh, A. H., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: AAAI, July 2013
Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: McKeown, K., Moore, J.D., Teufel, S., Allan, J., Furui, S. (eds.), Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, Columbus, Ohio, USA, 15–20 June 2008, ACL 2008, pp. 308–316. The Association for Computer Linguistics (2008)
Wei, W., Gulla, J.A.: Sentiment learning on product reviews via sentiment ontology tree. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 404–413. Association for Computational Linguistics, July 2010
Netowl About. Net Owl. N.p. (2016). https://www.netowl.com/our-story/. Accessed 7 Sept 2016
Kobdani, H., Schütze, H., Schiehlen, M., Kamp, H.: Bootstrapping coreference resolution using word associations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 783–792. Association for Computational Linguistics, June 2011
Haghighi, A., Klein, D.: Unsupervised coreference resolution in a nonparametric Bayesian model. In: Annual meeting-Association for Computational Linguistics, vol. 45, no. 1, p. 848, June 2007
Haghighi, A., Klein, D.: Simple coreference resolution with rich syntactic and semantic features. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3, pp. 1152–1161. Association for Computational Linguistics, August 2009
Bengtson, E., Roth, D.: Understanding the value of features for coreference resolution. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 294–303. Association for Computational Linguistics, October 2008
Nicolae, C., Nicolae, G.: BESTCUT: a graph algorithm for coreference resolution. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 275–283. Association for Computational Linguistics, July 2006
Quan, T.T., Hui, S.C.: Ontology-based natural query retrieval using conceptual graphs. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 309–320. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89197-0_30
Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Comput. Linguist. 27(4), 521–544 (2001)
Acknowledgments
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2016-20-36. We are also grateful to YouNet Media for supporting real datasets for our experiment.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Le, T.T., Vo, T.H., Mai, D.T., Quan, T.T., Phan, T.T. (2016). Sentiment Analysis Using Anaphoric Coreference Resolution and Ontology Inference. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-49397-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49396-1
Online ISBN: 978-3-319-49397-8
eBook Packages: Computer ScienceComputer Science (R0)