loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2009 Second International Symposium on Computational Intelligence and Design
Finer Granularity Clustering for Opinion Mining
Changsha, Hunan, China
December 12-December 14
ISBN: 978-0-7695-3865-5
ASCII Text
x 
 
Yin Luo, Gongqi Lin, Yan Fu, "Finer Granularity Clustering for Opinion Mining," Computational Intelligence and Design, International Symposium on, vol. 1, pp. 68-71, 2009 Second International Symposium on Computational Intelligence and Design, 2009.
 
 
BibTex
x
 
@article{ 10.1109/ISCID.2009.24,
author = {Yin Luo and Gongqi Lin and Yan Fu},
title = {Finer Granularity Clustering for Opinion Mining},
journal ={Computational Intelligence and Design, International Symposium on},
volume = {1},
year = {2009},
isbn = {978-0-7695-3865-5},
pages = {68-71},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISCID.2009.24},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computational Intelligence and Design, International Symposium on
TI - Finer Granularity Clustering for Opinion Mining
SN - 978-0-7695-3865-5
SP68
EP71
A1 - Yin Luo,
A1 - Gongqi Lin,
A1 - Yan Fu,
PY - 2009
KW - opinion mining
KW - Text mining
KW - Semantic Orientation
KW - finer granularity clustering
VL - 1
JA - Computational Intelligence and Design, International Symposium on
ER -
 
The boom of opinion-rich resources such as online review websites, discussion groups, personal blogs and forums on the web has attracted many research efforts on opinion mining. Positive and negative opinions represented in review documents are helpful information for governments to improve their services, for companies to market their products, and for customers to purchase their commodities. In this paper, we introduce a new approach that employs finer granularity clustering for opinions extraction and clustering for the calculation of their sentiment orientation of opinions. The experimental result shows that the approach is qualitatively quite useful when used to analyze the netizens’ opinions to hot topics from some websites.
Index Terms:
opinion mining, Text mining, Semantic Orientation, finer granularity clustering
Citation:
Yin Luo, Gongqi Lin, Yan Fu, "Finer Granularity Clustering for Opinion Mining," iscid, vol. 1, pp.68-71, 2009 Second International Symposium on Computational Intelligence and Design, 2009
Usage of this product signifies your acceptance of the Terms of Use.