Application of text-analytics in quantitative study of science and technology

Samira Ranaei; Arho Suominen; Alan Porter; Tuomo Kässi

doi:10.1007/978-3-030-02511-3_39

Application of text-analytics in quantitative study of science and technology

Samira Ranaei^*, Arho Suominen, Alan Porter, Tuomo Kässi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

11 Citations (Scopus)

Abstract

The quantitative study of science, technology and innovation (ST&I science, technology, and innovation (STI)) has experienced significant growth with advancements in disciplines such as mathematics, computer science and information sciences. From the early studies utilizing the statistics method, graph theory, to citations or co-authorship, the state of the art in quantitative methods leverages natural language processing and machine learning. However, there is no unified methodological approach within the research community or a comprehensive understanding of how to exploit text-mining potentials to address ST&I research objectives. Therefore, this chapter intends to present the state of the art of text mining within the framework of ST&I. The major contribution of the chapter is twofold; first, it provides a review of the literature on how text mining extended the quantitative methods applied in ST&I and highlights major methodological challenges. Second, it discusses two hands-on detailed case studies on how to implement the text analytics routine.

Original language	English
Title of host publication	Springer Handbook of Science and Technology Indicators
Editors	W. Glänzel, H.F. Moed, U. Schmoch, M. Thelwall
Publisher	Springer
Pages	957-982
ISBN (Electronic)	978-3-030-02511-3
ISBN (Print)	978-3-030-02510-6
DOIs	https://doi.org/10.1007/978-3-030-02511-3_39
Publication status	Published - 2019
MoE publication type	A3 Part of a book or another research book

Publication series

Series	Springer Handbooks
ISSN	2522-8692

Keywords

bibliometrics
literature review
machine learning
natural language processing
science mapping
scientometrics
text analytics
text-mining

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10.1007/978-3-030-02511-3_39

Cite this

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Application of text-analytics in quantitative study of science and technology. / Ranaei, Samira; Suominen, Arho; Porter, Alan et al.
Springer Handbook of Science and Technology Indicators. ed. / W. Glänzel; H.F. Moed; U. Schmoch; M. Thelwall. Springer, 2019. p. 957-982 (Springer Handbooks).

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

TY - CHAP

T1 - Application of text-analytics in quantitative study of science and technology

AU - Ranaei, Samira

AU - Suominen, Arho

AU - Porter, Alan

AU - Kässi, Tuomo

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N2 - The quantitative study of science, technology and innovation (ST&I science, technology, and innovation (STI)) has experienced significant growth with advancements in disciplines such as mathematics, computer science and information sciences. From the early studies utilizing the statistics method, graph theory, to citations or co-authorship, the state of the art in quantitative methods leverages natural language processing and machine learning. However, there is no unified methodological approach within the research community or a comprehensive understanding of how to exploit text-mining potentials to address ST&I research objectives. Therefore, this chapter intends to present the state of the art of text mining within the framework of ST&I. The major contribution of the chapter is twofold; first, it provides a review of the literature on how text mining extended the quantitative methods applied in ST&I and highlights major methodological challenges. Second, it discusses two hands-on detailed case studies on how to implement the text analytics routine.

AB - The quantitative study of science, technology and innovation (ST&I science, technology, and innovation (STI)) has experienced significant growth with advancements in disciplines such as mathematics, computer science and information sciences. From the early studies utilizing the statistics method, graph theory, to citations or co-authorship, the state of the art in quantitative methods leverages natural language processing and machine learning. However, there is no unified methodological approach within the research community or a comprehensive understanding of how to exploit text-mining potentials to address ST&I research objectives. Therefore, this chapter intends to present the state of the art of text mining within the framework of ST&I. The major contribution of the chapter is twofold; first, it provides a review of the literature on how text mining extended the quantitative methods applied in ST&I and highlights major methodological challenges. Second, it discusses two hands-on detailed case studies on how to implement the text analytics routine.

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KW - literature review

KW - machine learning

KW - natural language processing

KW - science mapping

KW - scientometrics

KW - text analytics

KW - text-mining

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PB - Springer

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Application of text-analytics in quantitative study of science and technology

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