Text mining for science and technology: A review - Part II-citation and discovery

Abstract

This is the second part of a two-part review that addresses three major text mining subdivisions: Characterization; seminal literature review (SLR); literature-related discovery and innovation (LRDI). Part I, published in the inaugural issue of The Journal of Scientometric Research (Kostoff 2012a), [1] focuses on characterization, mainly its non-citation components. Part II, published in this second issue of the Journal of Scientometric Research, focuses on the citation component of characterization, the citation-based SLR, and the citation-enabled LRDI.

Innovation, Internationalization and Entrepreneurship

“…For instance, these methods were adopted by the scholars investigating social media websites or industry 4.0 issues when big data sets were analysed (Pejic-Bach et al 2020). The selection of mixed research methods, such as an interdisciplinary and literature review, and text mining with a bibliometric analysis using data and characterization logic, are also applicable in different studies of management science (Kostoff 2012(Kostoff , 2013Youngblood and Lahti 2018). In general, the interdisciplinary mapping method is useful for identifying layers of the analysed concept as well as for a visualization of relationships between different approaches to MC among authors, a support of historical assumptions about the development of fields, and positioning the research in relation to previous works of MC (Aboelela et al 2007;Brandão et al 2017;Youngblood and Lahti 2018).…”

Mapping the Scientific Research on Mass Customization Domain: A Critical Review and Bibliometric Analysis

“…For instance, these methods were adopted by the scholars investigating social media websites or industry 4.0 issues when big data sets were analysed (Pejic-Bach et al 2020). The selection of mixed research methods, such as an interdisciplinary and literature review, and text mining with a bibliometric analysis using data and characterization logic, are also applicable in different studies of management science (Kostoff 2012(Kostoff , 2013Youngblood and Lahti 2018). In general, the interdisciplinary mapping method is useful for identifying layers of the analysed concept as well as for a visualization of relationships between different approaches to MC among authors, a support of historical assumptions about the development of fields, and positioning the research in relation to previous works of MC (Aboelela et al 2007;Brandão et al 2017;Youngblood and Lahti 2018).…”