Abstract
The feature extraction of opinions from online user reviews is a task to identify on which features user is going to write a review. There are number of existing approaches for opinion feature identification but, they are extracting features from a single review corpus. These techniques ignore the nontrivial disparities in distribution of words of opinion features across two or more corpora. This proposed work discusses a novel method for opinion feature identification from online reviews by evaluation of frequencies in two corpora, one is domain-specific and other is domain-independent corpus. This disparity is measured using domain relevance. The first task of this proposed work is to extract candidate features in user reviews by applying a set of syntactic dependence rules. The second task is to measure intrinsic domain relevance and extrinsic domain relevance scores on the domain-independent and domain-dependent corpora, respectively. The third task is to extract candidate features that are less generic and more domain-specific, are then conformed as opinion features.
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
L. Qu, G. Ifrim, and G.Weikum, “The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns”, Proc. 23rd Intl Conf. Computational Linguistics, pp. 913–921, 2010.
D. Bollegala, D.Weir, and J. Carroll, “Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus”, IEEE Trans. Knowledge and Data Eng., vol. 25, no. 8, pp. 1719–1731, Aug. 2013.
P.D. Turney, “Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews”, Proc. 40th Ann. Meeting on Assoc. for Computational Linguistics, pp. 417–424, 2002.
D.M. Blei, A.Y. Ng, and M.I. Jordan,“Latent Dirichlet Allocation”, J. Machine Learning Research, vol. 3, pp. 993–1022, Mar. 2003.
W.X. Zhao, J. Jiang, H. Yan, and X. Li, Jointly Modeling Aspects and Opinions with a Maxent-Lda Hybrid”, Proc. Conf. Empirical Methods in Natural Language Processing, pp. 56–65, 2010.
F. Fukumoto and Y. Suzuki, Event Tracking Based on Domain Dependency”, Proc. 23rd Ann. Intl ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 57–64, 2000.
G. Qiu, C.Wang, J. Bu, K. Liu, and C. Chen,“Incorporate the Syntactic Knowledge in Opinion Mining in User-Generated Content”, Proc. WWW 2008 Workshop NLP Challenges in the Information Explosion Era, 2008.
W. Jin and H.H. Ho,“A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining”, Proc. 26th Ann. Intl Conf. Machine Learning, pp. 465–472, 2009.
N. Jakob and I. Gurevych,“Extracting Opinion Targets in a Singleand Cross-Domain Setting with Conditional Random Fields”, Proc. Conf. Empirical Methods in Natural Language Processing, pp. 1035–1045, 2010.
G. Qiu, B. Liu, J. Bu, and C. Chen,“OpinionWord Expansion and Target Extraction through Double Propagation”, Computational Linguistics, vol. 37, pp. 9–27, 2011.
V. Hatzivassiloglou and J.M. Wiebe, “Effects of Adjective Orientation and Gradability on Sentence Subjectivity”, Proc. 18th Conf. Computational Linguistics, pp. 299–305, 2000.
B. Pang, L. Lee, and S. Vaithyanathan,“Thumbs up?: Sentiment Classification Using Machine Learning Techniques,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 79–86, 2002.
R. Mcdonald, K. Hannan, T. Neylon, M. Wells, and J. Reynar,“Structured Models for Fine-to-Coarse Sentiment Analysis”, Proc. 45th Ann. Meeting of the Assoc. of Computational Linguistics, pp. 432–439, 2007.
Zhen Hai, Kuiyu Chang, Jung-Jae Kim, and Christopher C. Yang,“Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 3, MARCH 2014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Jawahar Gawade, Latha Parthiban (2017). Opinion Mining Feature Extraction Using Domain Relevance. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 468. Springer, Singapore. https://doi.org/10.1007/978-981-10-1675-2_40
Download citation
DOI: https://doi.org/10.1007/978-981-10-1675-2_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1674-5
Online ISBN: 978-981-10-1675-2
eBook Packages: EngineeringEngineering (R0)