This paper performs a quantitative analysis of trends in technology mining (TM) approaches using 5 years (2011–2015) of Global TechMining (GTM) conference proceedings as a data source. These proceedings are processed with a help of Vantage Point software, providing an approach “tech mining for analyzing tech mining.” Through quantitative data processing (bibliometric analysis, natural language processing, statistical analysis, principal component analysis (PCA)), this study presents an overview, explores dynamics and potentials for existing and advanced TM methodologies in three layers: related methods, data sources, and software tools. The main groups and combinations of TM and related methods are identified. Key trends and weak signals concerning the use of existing (natural language processing (NLP), mapping, network analysis, etc.) and emerging methods (web scraping, ontology modeling, advanced bibliometrics, semantic the theory of inventive problem solving (TRIZ), sentiment analysis, etc.) are detected. The results are considered to be taken as a guide for researchers, practitioners, or policy makers involved in foresight activity.
Author(s): Nadezhda Mikova
Organization(s): Higher School of Economics
Source: Anticipating Future Innovation Pathways Through Large Data Analysis pp 59-69