Opinion Targets and Opinion Words Co-Extraction from Online Reviews Based on the Novel Approach with Partially Supervised Alignment Model

D. Charliena¹ , GK. Chakravarthi²

Section:Review Paper, Product Type: Isroset-Journal
Vol.4 , Issue.3 , pp.38-41, Jun-2016

Online published on Jul 02, 2016

Copyright © D. Charliena, GK. Chakravarthi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
One of the most significant tasks of opinion mining is mining opinion targets and opinion words from the online reviews. The major key component is to detect opinion relations between words. This paper proposes a novel approach based on the partially-supervised alignment model which identifies opinion relations as an alignment process. There after a graph-based co-ranking algorithm is made used, in order to estimate the confidence of each candidate. Lastly, candidates having higher confidence are extracted as the opinion targets or the opinion words. When compared with the other methods, this model is making the task of opinion relations for long-span relations. When compared to the syntax-based methods, our word alignment model effectively reduces the negative effects of the parsing errors when dealing with the informal online texts. So, when compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. When estimating the candidate confidence, we got to know that higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of generation of error.

Key-Words / Index Term :
Opinion mining; opinion targets extraction; opinion words extraction

References :
[1]. K. Liu, L. Xu, J. Zhao, “Co-extracting opinion targets and opinion words from online reviews based on the word alignment model”, IEEE Transactions on Knowledge and Data engineering, Vol.27, No,3, pp. 636-650, 2015.
[2]. Z. Hai, K. Chang, J.J. Kim, C.C. Yang, “Identifying features in opinion mining via intrinsic and extrinsic domain relevance,” IEEE Transaction on Knowledge Data Engineering,Vol.26, No,3, pp.623–634, 2014.
[3]. L. Zhang, B. Liu, S. H. Lim, and E. O’Brien-Strain, “Extracting and ranking product features in documents”, 3rd International Conference on Computational Linguistics, Beijing, pp.1462–1470, 2010.
[4]. R. Asritha , B.R. Reddy, "Opinion Target Extraction Using Word Based Alignment Model", International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, Issue.1, pp.97-101,2016
[5]. B. Pang, L. Lee, “Opinion mining and sentiment analysis”, Foundations and Trends in Information Retrieval, Vol.2, Issue1, pp.1-35, 2008.
[6]. Q. Mei, X. Ling, M. Wondra, H. Su, C. Zhai, “Topic sentiment mixture: modeling facets and opinions in weblogs”, 6th International conference on World Wide Web, Alberta, pp. 171–180, 2007.
[7]. F. Li, C. Han, M. Huang, X. Zhu, Y. Xia, S. Zhang, H. Yu, “Structure-aware review mining and summarization”, In Proceedings of the 23rd international conference on computational linguistics, China, pp. 653–661, 2010.