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http://dx.doi.org/10.9723/jksiis.2017.22.4.019

A Study on Science Technology Trend and Prediction Using Topic Modeling  

Park, Ju Seop (동아대학교 경영정보학과)
Hong, Soon-Goo (동아대학교 경영정보학과)
Kim, Jong-Weon (동의대학교 정보경영학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.22, no.4, 2017 , pp. 19-28 More about this Journal
Abstract
Companies and Governments have Mainly used the Delphi Technique to Understand Research or Technology Trends. Because this Technique has the Disadvantage of Consuming a Large Amount of Time and Money, this Study Attempted to Understand and Predict Science and Technology Trends using the Topic Modeling Technique Latent Dirichlet Allocation (LDA). To this end, 20 Specific Artificial Intelligence (AI) Technologies were Extracted From the Abstracts of the US Patent Documents on AI. With Regard to the Extracted Specific Technologies, Core Technologies were Identified, and then these were Divided into Hot and Cold Technologies though a Trend Analysis on their Annual Proportions. Text/Word Searching, Computer Management, Programming Syntax, Network Administration, Multimedia, and Wireless Network Technology were Derived From Hot Technologies. These Technologies are Key Technologies that are Actively Studied in the Field of AI in Recent Years. The Methodology Suggested in this Study may be used to Analyze Trends, Derive Policies, or Predict Technical Demands in Various Fields such as Social Issues, Regional Innovation, and Management.
Keywords
Science and Technology Predictions; Latent Dirichlet Allocation (LDA); Topic Modeling; Artificial Intelligence (AI); Patent Analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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