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Online Reviews Based on the Word Alignment Model
Authors(3) :-Sayali kothekar, Kalpana Malape, Vijaya Kamble
Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph-based co-ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words. Compared to previous methods based on the nearest-neighbor rules, our model captures opinion relations more precisely, especially for long-span relations. Compared to syntax-based methods, our word alignment model effectively alleviates the negative effects of parsing errors when dealing with informal online texts. In particular, compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. In addition, when estimating candidate confidence, we penalize higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of error generation. Our experimental results on three corpora with different sizes and languages show that our approach effectively outperforms state-of-the-art methods.
Sayali kothekar, Kalpana Malape, Vijaya Kamble
Opinion Mining, Opinion Targets Extraction, Opinion Words Extraction
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Published in : Volume 3 | Issue 2 | January-February 2017
Date of Publication : 2017-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 222-230
Manuscript Number : NCAEAS2351
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Sayali kothekar, Kalpana Malape, Vijaya Kamble, "Online Reviews Based on the Word Alignment Model", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.222-230, January-February-2017
URL : http://ijsrst.com/NCAEAS2351