Classify blog articles using queried keywords

Yi Hui Chen; Eric Jui Lin Lu; Tsai Ying Wu; Tsung Hau Lin

doi:10.3233/978-1-61499-484-8-1062

Classify blog articles using queried keywords

Yi Hui Chen, Eric Jui Lin Lu^*, Tsai Ying Wu, Tsung Hau Lin

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

According to a Technorati report, 40% of blog readers agree that blog content is better than content in mainstream media; 48% of bloggers believe network users will obtain knowledge through blogs within the next five years. However, blogs still exist like isolated islands. One approach to break this barrier is to use a clustering technique to link similar articles together. Traditionally, clustering methods utilize keywords extracted from whole articles. Because queried keywords represent user's intentions, we believed they provide a better foundation for clustering methods.The experimental results show that the average precision and recall values of clustering methods based on queried keywords perform better than those of using social tagging and keywords extracted from whole articles. In addition, FCA+HAC using queried keywords outperforms other studied methods.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
Editors	William Cheng-Chung Chu, Han-Chieh Chao, Stephen Jenn-Hwa Yang
Publisher	IOS Press BV
Pages	1062-1068
Number of pages	7
ISBN (Electronic)	9781614994831
DOIs	https://doi.org/10.3233/978-1-61499-484-8-1062
State	Published - 2015
Externally published	Yes
Event	International Computer Symposium, ICS 2014 - Taichung, Taiwan Duration: 12 12 2014 → 14 12 2014

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	274
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Conference

Conference	International Computer Symposium, ICS 2014
Country/Territory	Taiwan
City	Taichung
Period	12/12/14 → 14/12/14

Bibliographical note

Publisher Copyright:
© 2015 The authors and IOS Press. All rights reserved.

Keywords

Formal concept analysis
blog clustering
queried keywords
social tagging

Access to Document

10.3233/978-1-61499-484-8-1062

Cite this

Chen, Y. H., Lu, E. J. L., Wu, T. Y., & Lin, T. H. (2015). Classify blog articles using queried keywords. In W. C.-C. Chu, H.-C. Chao, & S. J.-H. Yang (Eds.), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014 (pp. 1062-1068). (Frontiers in Artificial Intelligence and Applications; Vol. 274). IOS Press BV. https://doi.org/10.3233/978-1-61499-484-8-1062

Chen, Yi Hui ; Lu, Eric Jui Lin ; Wu, Tsai Ying et al. / Classify blog articles using queried keywords. Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. editor / William Cheng-Chung Chu ; Han-Chieh Chao ; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. pp. 1062-1068 (Frontiers in Artificial Intelligence and Applications).

@inproceedings{22266166dd5440e6b572513ac6344047,

title = "Classify blog articles using queried keywords",

abstract = "According to a Technorati report, 40% of blog readers agree that blog content is better than content in mainstream media; 48% of bloggers believe network users will obtain knowledge through blogs within the next five years. However, blogs still exist like isolated islands. One approach to break this barrier is to use a clustering technique to link similar articles together. Traditionally, clustering methods utilize keywords extracted from whole articles. Because queried keywords represent user's intentions, we believed they provide a better foundation for clustering methods.The experimental results show that the average precision and recall values of clustering methods based on queried keywords perform better than those of using social tagging and keywords extracted from whole articles. In addition, FCA+HAC using queried keywords outperforms other studied methods.",

keywords = "Formal concept analysis, blog clustering, queried keywords, social tagging",

author = "Chen, {Yi Hui} and Lu, {Eric Jui Lin} and Wu, {Tsai Ying} and Lin, {Tsung Hau}",

year = "2015",

doi = "10.3233/978-1-61499-484-8-1062",

language = "英语",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press BV",

pages = "1062--1068",

editor = "Chu, {William Cheng-Chung} and Han-Chieh Chao and Yang, {Stephen Jenn-Hwa}",

booktitle = "Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014",

address = "荷兰",

}

Chen, YH, Lu, EJL, Wu, TY & Lin, TH 2015, Classify blog articles using queried keywords. in WC-C Chu, H-C Chao & SJ-H Yang (eds), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. Frontiers in Artificial Intelligence and Applications, vol. 274, IOS Press BV, pp. 1062-1068, International Computer Symposium, ICS 2014, Taichung, Taiwan, 12/12/14. https://doi.org/10.3233/978-1-61499-484-8-1062

Classify blog articles using queried keywords. / Chen, Yi Hui; Lu, Eric Jui Lin; Wu, Tsai Ying et al.
Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. ed. / William Cheng-Chung Chu; Han-Chieh Chao; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. p. 1062-1068 (Frontiers in Artificial Intelligence and Applications; Vol. 274).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classify blog articles using queried keywords

AU - Chen, Yi Hui

AU - Lu, Eric Jui Lin

AU - Wu, Tsai Ying

AU - Lin, Tsung Hau

PY - 2015

Y1 - 2015

N2 - According to a Technorati report, 40% of blog readers agree that blog content is better than content in mainstream media; 48% of bloggers believe network users will obtain knowledge through blogs within the next five years. However, blogs still exist like isolated islands. One approach to break this barrier is to use a clustering technique to link similar articles together. Traditionally, clustering methods utilize keywords extracted from whole articles. Because queried keywords represent user's intentions, we believed they provide a better foundation for clustering methods.The experimental results show that the average precision and recall values of clustering methods based on queried keywords perform better than those of using social tagging and keywords extracted from whole articles. In addition, FCA+HAC using queried keywords outperforms other studied methods.

AB - According to a Technorati report, 40% of blog readers agree that blog content is better than content in mainstream media; 48% of bloggers believe network users will obtain knowledge through blogs within the next five years. However, blogs still exist like isolated islands. One approach to break this barrier is to use a clustering technique to link similar articles together. Traditionally, clustering methods utilize keywords extracted from whole articles. Because queried keywords represent user's intentions, we believed they provide a better foundation for clustering methods.The experimental results show that the average precision and recall values of clustering methods based on queried keywords perform better than those of using social tagging and keywords extracted from whole articles. In addition, FCA+HAC using queried keywords outperforms other studied methods.

KW - Formal concept analysis

KW - blog clustering

KW - queried keywords

KW - social tagging

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A2 - Chao, Han-Chieh

A2 - Yang, Stephen Jenn-Hwa

PB - IOS Press BV

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Y2 - 12 December 2014 through 14 December 2014

ER -

Classify blog articles using queried keywords

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Keywords

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