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An analysis of text mining factors enhancing the identification of relevant studies

The development of science and the spread of knowledge coincide with growing number of publications, and the volume of online content continue to grow at a rapid rate. For some submitted queries, the search engines may return thousands of documents of questionable relevancy. In this paper, we analyz...

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Main Authors: Khashfeh M., Mahmoud M.A., Ahmad M.S.
Other Authors: 57202812898
Format: Article
Published: Little Lion Scientific 2023
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spelling my.uniten.dspace-237892023-05-29T14:51:50Z An analysis of text mining factors enhancing the identification of relevant studies Khashfeh M. Mahmoud M.A. Ahmad M.S. 57202812898 55247787300 56036880900 The development of science and the spread of knowledge coincide with growing number of publications, and the volume of online content continue to grow at a rapid rate. For some submitted queries, the search engines may return thousands of documents of questionable relevancy. In this paper, we analyze the literature and identify the text mining factors that influence the identification of relevant studies. Five factors are identified which are Text Typography; Paragraph length; Term Frequency factor; Coordination; and Strict search. Subsequently, we propose an agent based-text mining model that facilitate the identification of relevant studies in big databases. The model consists of four components which are, interface, search process, parsing process, and storage. The interface provides a communication mean between a user and his/her counterpart agent (Personal Agent). In addition, it provides an input tool for user�s search preferences. The second component is the search process that is operated by a pattern matching. The third process is the parsing that is operated by a text mining algorithm. The last part is the storage that is managed by Monitor Agent. The proposed framework would be useful in providing an alternative means of searching highly relevant studies from large databases. � 2005 - ongoing JATIT & LLS. Final 2023-05-29T06:51:50Z 2023-05-29T06:51:50Z 2018 Article 2-s2.0-85049435241 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049435241&partnerID=40&md5=80d1d8e3c805438c9e0112017c5509ae https://irepository.uniten.edu.my/handle/123456789/23789 96 12 3896 3907 Little Lion Scientific Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description The development of science and the spread of knowledge coincide with growing number of publications, and the volume of online content continue to grow at a rapid rate. For some submitted queries, the search engines may return thousands of documents of questionable relevancy. In this paper, we analyze the literature and identify the text mining factors that influence the identification of relevant studies. Five factors are identified which are Text Typography; Paragraph length; Term Frequency factor; Coordination; and Strict search. Subsequently, we propose an agent based-text mining model that facilitate the identification of relevant studies in big databases. The model consists of four components which are, interface, search process, parsing process, and storage. The interface provides a communication mean between a user and his/her counterpart agent (Personal Agent). In addition, it provides an input tool for user�s search preferences. The second component is the search process that is operated by a pattern matching. The third process is the parsing that is operated by a text mining algorithm. The last part is the storage that is managed by Monitor Agent. The proposed framework would be useful in providing an alternative means of searching highly relevant studies from large databases. � 2005 - ongoing JATIT & LLS.
author2 57202812898
author_facet 57202812898
Khashfeh M.
Mahmoud M.A.
Ahmad M.S.
format Article
author Khashfeh M.
Mahmoud M.A.
Ahmad M.S.
spellingShingle Khashfeh M.
Mahmoud M.A.
Ahmad M.S.
An analysis of text mining factors enhancing the identification of relevant studies
author_sort Khashfeh M.
title An analysis of text mining factors enhancing the identification of relevant studies
title_short An analysis of text mining factors enhancing the identification of relevant studies
title_full An analysis of text mining factors enhancing the identification of relevant studies
title_fullStr An analysis of text mining factors enhancing the identification of relevant studies
title_full_unstemmed An analysis of text mining factors enhancing the identification of relevant studies
title_sort analysis of text mining factors enhancing the identification of relevant studies
publisher Little Lion Scientific
publishDate 2023
_version_ 1806427835906129920
score 13.211869

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