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Opinion Word Expansion and Target Extraction through Double Propagation

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Title: Opinion Word Expansion and Target Extraction through Double Propagation
Author(s): Qiu, Guang; Liu, Bing; Bu, Jiajun; Chen, Chun
Subject(s): opinion target double propagation opinion expansion
Abstract: Analysis of opinions, known as opinion mining or sentiment analysis, has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this article, we study two important problems, namely, opinion lexicon expansion and opinion target extraction. Opinion targets (targets, for short) are entities and their attributes on which opinions have been expressed. To perform the tasks, we found that there are several syntactic relations that link opinion words and targets. These relations can be identified using a dependency parser and then utilized to expand the initial opinion lexicon and to extract targets. This proposed method is based on bootstrapping. We call it double propagation as it propagates information between opinion words and targets. A key advantage of the proposed method is that it only needs an initial opinion lexicon to start the bootstrapping process. Thus, the method is semi-supervised due to the use of opinion word seeds. In evaluation, we compare the proposed method with several state-of-the-art methods using a standard product review test collection. The results show that our approach outperforms these existing methods significantly.
Issue Date: 2011-03
Publisher: Massachusetts Institute of Technology Press
Citation Info: Qiu, G. A., Liu, B., Bu, J. J., & Chen, C. 2011. Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 37(1): 9-27. DOI: 10.1162/coli_a_00034
Type: Article
Description: This is a copy of an article published in Computational Linguistics © 2011 Copyright MIT Press. Computational Linguistics is available online at http://www.mitpressjournals.org/toc/coli/37/1; DOI: 10.1162/coli_a_00034.
URI: http://hdl.handle.net/10027/8193
ISSN: 0891-2017
Sponsor: This work was supported by National Key Technology R&D Programs 2008BAH26B00 and 2007BAH11B06.
Date Available in INDIGO: 2012-03-09
 

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