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Application of Text-Analytics in Quantitative Study of Science and Technology

  • Samira RanaeiEmail author
  • Arho Suominen
  • Alan Porter
  • Tuomo Kässi
Chapter
Part of the Springer Handbooks book series (SHB)

Abstract

The quantitative study of science, technology and innovation (ST&I ) 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.

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

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Samira Ranaei
    • 1
    Email author
  • Arho Suominen
    • 2
  • Alan Porter
    • 3
  • Tuomo Kässi
    • 1
  1. 1.School of Engineering Science, Industrial Engineering and ManagementLappeenranta University of Technology (LUT)LappeenrantaFinland
  2. 2.VTT Technical Research Centre of FinlandEspooFinland
  3. 3.Search Technology, Inc.Norcross, GAUSA

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