Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research

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Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research

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作者：Yi Zhang^a ; ^b ; ^{yizhang.bit@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Guangquan Zhang^a ; ^{guangquan.zhang@uts.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Hongshu Chen^a ; ^b ; ^{Hongshu.Chen@student.uts.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Alan L. Porter^c ; ^{alan.porter@isye.gatech.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Donghua Zhu^b ; ^{zhudh111@bit.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jie Lu^a ; ^{Jie.Lu@uts.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae
关键词：Topic analysis ; Technological forecasting ; Text mining ; Text clustering ; Technical intelligence
刊名：Technological Forecasting & Social Change
出版年：2016
出版时间：April 2016
年：2016
卷：105
期：Complete
页码：179-191
全文大小：967 K

文摘

•: Data-driven clustering approach to group topics with high accuracy
•: Similarity measure approach to trace the interaction between topics in time series
•: Analyzing changes of TFIDF values of related topics to predict future trends
•: Technology Roadmapping to blend historical analysis and expert-based forecasting

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