Text mining for Indonesian translation of the Quran: A systematic review

2017 ◽  
Author(s):  
Syopiansyah Jaya Putra ◽  
Teddy Mantoro ◽  
Muhamad Nur Gunawan
Keyword(s):  
Systematic Review ◽  
Text Mining

Knowledge discovery out of text data: a systematic review via text mining

2018 ◽  
Vol 22 (7) ◽  
pp. 1471-1488 ◽  
Author(s):  
Antonio Usai ◽  
Marco Pironti ◽  
Monika Mital ◽  
Chiraz Aouina Mejri
Keyword(s):  
Systematic Review ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Research Collaboration ◽  
Collaborative Writing ◽  
Web Based ◽  
Diverse Range ◽  
Content Type ◽  
Mining Technique ◽  
Database Technology

Purpose The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics. Design/methodology/approach This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1). Findings The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment. Originality/value This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

scholarly journals Erratum to: Using text mining for study identification in systematic reviews: a systematic review of current approaches

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Alison O’Mara-Eves ◽  
James Thomas ◽  
John McNaught ◽  
Makoto Miwa ◽  
Sophia Ananiadou
Keyword(s):  
Systematic Review ◽  
Text Mining ◽  
Systematic Reviews

Text mining for market prediction: A systematic review

2014 ◽  
Vol 41 (16) ◽  
pp. 7653-7670 ◽  
Author(s):  
Arman Khadjeh Nassirtoussi ◽  
Saeed Aghabozorgi ◽  
Teh Ying Wah ◽  
David Chek Ling Ngo
Keyword(s):  
Systematic Review ◽  
Text Mining

scholarly journals Using text mining for study identification in systematic reviews: a systematic review of current approaches

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Alison O’Mara-Eves ◽  
James Thomas ◽  
John McNaught ◽  
Makoto Miwa ◽  
Sophia Ananiadou
Keyword(s):  
Systematic Review ◽  
Text Mining ◽  
Systematic Reviews

scholarly journals Epistemological Considerations of Text Mining: Implications for Systematic Literature Review

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1865
Author(s):  
Daniel Caballero-Julia ◽  
Philippe Campillo
Keyword(s):  
Systematic Review ◽  
Multivariate Analysis ◽  
Text Mining ◽  
Large Set ◽  
Review Of The Literature ◽  
Mathematical Solution ◽  
Advantages And Disadvantages ◽  
Mathematical Calculation ◽  
The Individual ◽  
Day By Day

In the era of big data, the capacity to produce textual documents is increasing day by day. Our ability to generate large amounts of information has impacted our lives at both the individual and societal levels. Science has not escaped this evolution either, and it is often difficult to quickly and reliably “stand on the shoulders of giants”. Text mining is presented as a promising mathematical solution. However, it has not yet convinced qualitative analysts who are usually wary of mathematical calculation. For this reason, this article proposes to rethink the epistemological principles of text mining, by returning to the qualitative analysis of its meaning and structure. It presents alternatives, applicable to the process of constructing lexical matrices for the analysis of a complex textual corpus. At the same time, the need for new multivariate algorithms capable of integrating these principles is discussed. We take a practical example in the use of text mining, by means of Multivariate Analysis of Variance Biplot (MANOVA-Biplot) when carrying out a systematic review of the literature. The article will show the advantages and disadvantages of exploring and analyzing a large set of publications quickly and methodically.

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