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

Samira Ranaei (Corresponding author), Arho Suominen, Alan Porter, Tuomo Kässi

    Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

    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 languageEnglish
    Title of host publicationSpringer Handbook of Science and Technology Indicators
    EditorsW. Glänzel, H.F. Moed, U. Schmoch, M. Thelwall
    PublisherSpringer
    Pages957-982
    ISBN (Electronic)978-3-030-02511-3
    ISBN (Print)978-3-030-02510-6
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA3 Part of a book or another research book

    Publication series

    SeriesSpringer Handbooks
    ISSN2522-8692

    Keywords

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

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