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.
Author(s): Samira Ranaei, Arho Suominen, Alan Porter, Tuomo Kässi
Organization(s): Lappeenranta University of Technology (LUT), VTT Technical Research Centre of Finland
Source: Springer Handbook of Science and Technology Indicators