@inbook{cf1859a427c3430cb5168120337cfcbe,
title = "Application of text-analytics in quantitative study of science and technology",
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.",
keywords = "bibliometrics, literature review, machine learning, natural language processing, science mapping, scientometrics, text analytics, text-mining",
author = "Samira Ranaei and Arho Suominen and Alan Porter and Tuomo K{\"a}ssi",
note = "Funding Information: Over two-thirds of the papers in our sample utilized either patents or scientific publications as major data sources (approximately 107 records). The remaining records exploited other sources of knowledge to provide evidence for ST&I research. For instance, content analysis by the National Science Foundation (NSF) provided new information for measuring interdisciplinar-ity [39.85], novel technological solutions, and new ideas derived from descriptions of research projects awarded grants from the National Institute of Standards and Technology (NIST) in the United States [39.93] or the Ministry of Defense in Germany [39.36]. For capturing interest in an emerging research topic from outside the academic domain, data sources other than patents and scientific publications have been explored using text analytics. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",
year = "2019",
doi = "10.1007/978-3-030-02511-3_39",
language = "English",
isbn = "978-3-030-02510-6",
series = "Springer Handbooks",
publisher = "Springer",
pages = "957--982",
editor = "W. Gl{\"a}nzel and H.F. Moed and U. Schmoch and M. Thelwall",
booktitle = "Springer Handbook of Science and Technology Indicators",
address = "Germany",
}