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Text content analysis for illicit web pages by using neural networks

Illicit web contents such as pornography, violence, and gambling have greatly polluted the mind of web users especially children and teenagers. Due to the ineffectiveness of some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection...

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Main Authors:	Lee, Zhi Sam, Maarof, Mohd. Aizaini, Selamat, Ali, Shamsuddin, Siti Mariyam
Format:	Article
Language:	English English
Published:	Penerbit UTM Press 2009
Subjects:	Q Science (General) QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/21030/1/AliSelamat2009_TextContentAnalysisforIllicit.pdf http://eprints.utm.my/id/eprint/21030/2/jurnalteknologi/article/view/168 http://eprints.utm.my/id/eprint/21030/
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spelling	my.utm.210302017-11-01T04:17:20Z http://eprints.utm.my/id/eprint/21030/ Text content analysis for illicit web pages by using neural networks Lee, Zhi Sam Maarof, Mohd. Aizaini Selamat, Ali Shamsuddin, Siti Mariyam Q Science (General) QA75 Electronic computers. Computer science Illicit web contents such as pornography, violence, and gambling have greatly polluted the mind of web users especially children and teenagers. Due to the ineffectiveness of some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking against today's dynamic web contents, content based analysis techniques with effective model are highly desired. In this paper, we have proposed a textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages. We have examined the entropy scheme with two other common term weighting schemes that are TFIDF and Glasgow. Those techniques have been tested with artificial neural network using small class dataset. In this study, we found that our proposed model has achieved better performance in terms accuracy, convergence speed, and stability compared to the other techniques. Penerbit UTM Press 2009-06 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/21030/1/AliSelamat2009_TextContentAnalysisforIllicit.pdf text/html en http://eprints.utm.my/id/eprint/21030/2/jurnalteknologi/article/view/168 Lee, Zhi Sam and Maarof, Mohd. Aizaini and Selamat, Ali and Shamsuddin, Siti Mariyam (2009) Text content analysis for illicit web pages by using neural networks. Jurnal Teknologi, 50 (D). pp. 73-91. ISSN 2180-3722 DOI:10.11113/jt.v50.168
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English English
topic	Q Science (General) QA75 Electronic computers. Computer science
spellingShingle	Q Science (General) QA75 Electronic computers. Computer science Lee, Zhi Sam Maarof, Mohd. Aizaini Selamat, Ali Shamsuddin, Siti Mariyam Text content analysis for illicit web pages by using neural networks
description	Illicit web contents such as pornography, violence, and gambling have greatly polluted the mind of web users especially children and teenagers. Due to the ineffectiveness of some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking against today's dynamic web contents, content based analysis techniques with effective model are highly desired. In this paper, we have proposed a textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages. We have examined the entropy scheme with two other common term weighting schemes that are TFIDF and Glasgow. Those techniques have been tested with artificial neural network using small class dataset. In this study, we found that our proposed model has achieved better performance in terms accuracy, convergence speed, and stability compared to the other techniques.
format	Article
author	Lee, Zhi Sam Maarof, Mohd. Aizaini Selamat, Ali Shamsuddin, Siti Mariyam
author_facet	Lee, Zhi Sam Maarof, Mohd. Aizaini Selamat, Ali Shamsuddin, Siti Mariyam
author_sort	Lee, Zhi Sam
title	Text content analysis for illicit web pages by using neural networks
title_short	Text content analysis for illicit web pages by using neural networks
title_full	Text content analysis for illicit web pages by using neural networks
title_fullStr	Text content analysis for illicit web pages by using neural networks
title_full_unstemmed	Text content analysis for illicit web pages by using neural networks
title_sort	text content analysis for illicit web pages by using neural networks
publisher	Penerbit UTM Press
publishDate	2009
url	http://eprints.utm.my/id/eprint/21030/1/AliSelamat2009_TextContentAnalysisforIllicit.pdf http://eprints.utm.my/id/eprint/21030/2/jurnalteknologi/article/view/168 http://eprints.utm.my/id/eprint/21030/
_version_	1643647249754882048
score	13.211869

Text content analysis for illicit web pages by using neural networks

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