Comparing SVD and SDAE for Analysis of Islamist Forum Postings

Alsadhan, N; Skillicorn, DB

Comparing SVD and SDAE for Analysis of Islamist Forum Postings

Alsadhan, N Skillicorn, DB

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, 2016, pp. 948 - 953
Issue Date:: 2016-01-29

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Field	Value	Language
dc.contributor.author	Alsadhan, N	en_US
dc.contributor.author	Skillicorn, DB	en_US
dc.date.issued	2016-01-29	en_US
dc.identifier.citation	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, 2016, pp. 948 - 953	en_US
dc.identifier.isbn	9781467384926	en_US
dc.identifier.uri	http://hdl.handle.net/10453/101259
dc.description.abstract	© 2015 IEEE. We analyze postings in the Turn to Islam forum using techniques based on singular value decomposition (SVD) and the deep learning technique of stacked denoising autoencoders (SDAE). Models based on frequent words and jihadist language intensity are used, and the results compared. Our main conclusion is that SDAE approaches, while clearly discovering structure in document-word matrices, do not yet provide a natural interpretation strategy, limiting their practical usefulness. In contrast, SVD approaches provide interpretable models, primarily because of the coupling between document and word variation patterns.	en_US
dc.relation.ispartof	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015	en_US
dc.relation.isbasedon	10.1109/ICDMW.2015.108	en_US
dc.title	Comparing SVD and SDAE for Analysis of Islamist Forum Postings	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. We analyze postings in the Turn to Islam forum using techniques based on singular value decomposition (SVD) and the deep learning technique of stacked denoising autoencoders (SDAE). Models based on frequent words and jihadist language intensity are used, and the results compared. Our main conclusion is that SDAE approaches, while clearly discovering structure in document-word matrices, do not yet provide a natural interpretation strategy, limiting their practical usefulness. In contrast, SVD approaches provide interpretable models, primarily because of the coupling between document and word variation patterns.

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http://hdl.handle.net/10453/101259