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...

Full description

Saved in:
Bibliographic Details
Main Authors: Khashfeh M., Mahmoud M.A., Ahmad M.S.
Other Authors: 57202812898
Format: Article
Published: Little Lion Scientific 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.