Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research
作者： Yi Zhang , Guangquan Zhang , Hongshu Chen , Alan L. Porter , Donghua Zhu
DOI: 10.1016/J.TECHFORE.2016.01.015
关键词: Technical intelligence 、 Document clustering 、 Computer-mediated communication 、 Foundation (evidence) 、 Topic analysis 、 Computer science 、 Big data 、 Science policy 、 Technology forecasting 、 Data science
摘要: Abstract The number and extent of current Science, Technology & Innovation topics are changing all the time, their induced accumulative innovation, or even disruptive revolution, will heavily influence whole society in near future. By addressing predicting these changes, this paper proposes an analytic method to (1) cluster associated terms phrases constitute meaningful technological interactions, (2) identify topical emphases. Our results carried forward present mechanisms that forecast prospective developments using Roadmapping, combining qualitative quantitative methodologies. An empirical case study Awards data from United States National Science Foundation, Division Computer Communication is performed demonstrate proposed method. resulting knowledge may hold interest for R&D management science policy practice.
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