Please use this identifier to cite or link to this item:
Title: Learning knowledge from relevant webpage for opinion analysis
Authors: Xu, R
Wong, KF
Lu, Q 
Xia, Y
Li, WJ 
Keywords: Linguistic Knowledge
Opinion Analysis
Unsupervised Learning
Issue Date: 2008
Publisher: IEEE
Source: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008 : WI-IAT '08, 9-12 December 2008, Sydney, NSW, p. 307-313 How to cite?
Abstract: This paper presents an opinion analysis system based on linguistic knowledge which is acquired from small-scale annotated text and raw topic-relevant Web page. Based on the observation on the annotated opinion corpus, some word-, collocation- and sentence-level linguistic features for opinion analysis are discovered. Supervised and unsupervised learning techniques are developed to learn these features from annotated text and raw relevant Web page, respectively. These features are then incorporated into a classifier based on support vector machine (SVM) to identify opinionated sentences and determine their polarities. Evaluations show that the proposed opinion analysis system, namely OA, achieved promising performance, which shows the effectiveness of linguistic knowledge learning from relevant Web page.
ISBN: 978-0-7695-3496-1
DOI: 10.1109/WIIAT.2008.388
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Feb 13, 2019

Page view(s)

Last Week
Last month
Citations as of Feb 17, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.