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

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dc.citedby1
dc.contributor.authorKhashfeh M.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorAhmad M.S.en_US
dc.contributor.authorid57202812898en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid56036880900en_US
dc.date.accessioned2023-05-29T06:51:50Z
dc.date.available2023-05-29T06:51:50Z
dc.date.issued2018
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.epage3907
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85049435241
dc.identifier.spage3896
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85049435241&partnerID=40&md5=80d1d8e3c805438c9e0112017c5509ae
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23789
dc.identifier.volume96
dc.publisherLittle Lion Scientificen_US
dc.sourceScopus
dc.sourcetitleJournal of Theoretical and Applied Information Technology
dc.titleAn analysis of text mining factors enhancing the identification of relevant studiesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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