Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28031
Title: Sentiment classification and polarity shifting
Authors: Li, S
Lee, SYM 
Chen, Y
Huang, CR 
Zhou, G
Issue Date: 2010
Source: Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 2010, v. 2, p. 635-643 How to cite?
Abstract: Polarity shifting marked by various linguistic structures has been a challenge to automatic sentiment classification. In this paper, we propose a machine learning approach to incorporate polarity shifting information into a document-level sentiment classification system. First, a feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences. Then, by using the obtained binary classifier, each document in the original polarity classification training data is split into two partitions, polarity-shifted and polarity-unshifted, which are used to train two base classifiers respectively for further classifier combination. The experimental results across four different domains demonstrate the effectiveness of our approach.
Description: 23rd International Conference on Computational Linguistics, Coling 2010, Beijing, 23-27 August 2010
URI: http://hdl.handle.net/10397/28031
Appears in Collections:Conference Paper

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

SCOPUSTM   
Citations

34
Last Week
18
Last month
Citations as of Feb 26, 2017

Page view(s)

42
Last Week
0
Last month
Checked on Aug 21, 2017

Google ScholarTM

Check



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