Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection

Social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Social networks, such as Twitter, are common mechanisms through which people can share information. The utilization of data that are available through social media for many applicat...

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Main Author:	Alsmadi, Issa Mohammad Ibrahim
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/46679/1/short%20text%20classification%20using%20an%20enhanced%20term%20weghiting%20scheme%20and%20filter-wrapper%20feture%20selection24.pdf
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id	my-usm-ep.46679
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spelling	my-usm-ep.466792020-07-03T07:41:20Z Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection 2018-12 Alsmadi, Issa Mohammad Ibrahim QA75.5-76.95 Electronic computers. Computer science Social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Social networks, such as Twitter, are common mechanisms through which people can share information. The utilization of data that are available through social media for many applications is gradually increasing. Redundancy and noise in short texts are common problems in social media and in different applications that use short text. However, the shortness and high sparsity of short text lead to poor classification performance. Employing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. This research aims to investigate and develop solutions for feature discrimination and selection in short texts classification. For feature discrimination, we introduce a term weighting approach namely, simple supervised weight (SW), which considers the special nature of short text in terms of term strength and distribution. To address the drawbacks of using existing feature selection with short text, this thesis proposes a filter-wrapper feature selection approach. In the first stage, we propose an adaptive filter-based feature selection method that is derived from the odd ratio method, used in reducing the dimensionality of feature space. In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature. 2018-12 Thesis http://eprints.usm.my/46679/ http://eprints.usm.my/46679/1/short%20text%20classification%20using%20an%20enhanced%20term%20weghiting%20scheme%20and%20filter-wrapper%20feture%20selection24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Alsmadi, Issa Mohammad Ibrahim Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
description	Social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Social networks, such as Twitter, are common mechanisms through which people can share information. The utilization of data that are available through social media for many applications is gradually increasing. Redundancy and noise in short texts are common problems in social media and in different applications that use short text. However, the shortness and high sparsity of short text lead to poor classification performance. Employing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. This research aims to investigate and develop solutions for feature discrimination and selection in short texts classification. For feature discrimination, we introduce a term weighting approach namely, simple supervised weight (SW), which considers the special nature of short text in terms of term strength and distribution. To address the drawbacks of using existing feature selection with short text, this thesis proposes a filter-wrapper feature selection approach. In the first stage, we propose an adaptive filter-based feature selection method that is derived from the odd ratio method, used in reducing the dimensionality of feature space. In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Alsmadi, Issa Mohammad Ibrahim
author_facet	Alsmadi, Issa Mohammad Ibrahim
author_sort	Alsmadi, Issa Mohammad Ibrahim
title	Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
title_short	Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
title_full	Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
title_fullStr	Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
title_full_unstemmed	Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
title_sort	short text classification using an enhanced term weighting scheme and filter-wrapper feature selection
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2018
url	http://eprints.usm.my/46679/1/short%20text%20classification%20using%20an%20enhanced%20term%20weghiting%20scheme%20and%20filter-wrapper%20feture%20selection24.pdf
_version_	1747821711103885312

Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection

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