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Text mining based theme logic structure identification: application in library journals

Document Cited in Related
DOIhttps://doi.org/10.1108/LHT-10-2017-0211
Published date17 September 2018
Date17 September 2018
Pages411-425
AuthorQing Zhu,Yiqiong Wu,Yuze Li,Jing Han,Xiaoyang Zhou
Text mining based theme logic
structure identification:
application in library journals
Qing Zhu and Yiqiong Wu
Institute of Cross-Process Perception and Control,
Shaanxi Normal University, Xi’an, China
Yuze Li
Department of Mechanical and Industrial Engineering,
University of Toronto, Toronto, Canada
Jing Han
Institute of Cross-Process Perception and Control,
Shaanxi Normal University, Xi’an, China, and
Xiaoyang Zhou
Institute of Cross-Process Perception and Control, Shaanxi Normal University,
Xi’an, China and
Academy of Mathematics and Systems Science, Chinese Academy of Sciences,
Beijing, China
Abstract
Purpose –Library intelligence institutions, which are a kind of traditional knowledge management
organization, are at the frontline of the big data revolution, in which the use of unstructured data has become
a modern knowledge management resource. The paper aims to discuss this issue.
Design/methodology/approach –This research combined theme logic structure (TLS), artificial neural
network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a
signal-wave to examine the research characteristics.
Findings –Research characteristics have a vital effect on knowledge management activities and
management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution.
Knowledge management should actively control the evolution of the research characteristics because the
natural development of six to nine years was found to be difficult to plot.
Originality/value –Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and
allows journal managers and contributors to follow the intrinsic mode functions and predict the journal
research characteristics tendencies.
Keywords Big data, Knowledge management, Machine learning, Text mining, ANN, EEMD
Paper type Research paper
1. Introduction
The rapid developments in mobile communications technology and the commensurate rise
inbigdatahaveresultedinalarge-scaletechnological revolution in information
interactions and applications (Chen et al., 2014; Hashem et al., 2016; Issam et al., 2014;
Library Hi Tech
Vol. 36 No. 3, 2018
pp. 411-425
Emerald Publishing Limited
0737-8831
DOI 10.1108/LHT-10-2017-0211
Received 22 October 2017
Revised 16 December 2017
17 December 2017
31 December 2017
Accepted 31 December 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
© Qing Zhu, Yiqiong Wu, Yuze Li, Jing Han and Xiaoyang Zhou. Published by Emerald Publishing
Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone
may reproduce, distribute, translate and create derivative works of this article (for both commercial
and non-commercial purposes), subject to full attribution to the original publication and authors. The
full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
The authors thanks those that have given constructive comments and feedback to help improve this
paper. Supported was provided by the National Natural Science Foundation of China (71401093,
71350007, 91646113).
411
Text mining
based TLS
identification

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