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A Semi-Automatic Emerging Technology Trend Classifier Using SCOPUS and PATSTAT

Seonho Kim, Woondong Yeo, Byong-Youl Coh, Waqas Rasheed, Jaewoo Kang


Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS [1] and PATSTAT [2]. Unlike most previous research, our emerging technology trend classifier utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends


SCOPUS, PATSTAT, Emerging trend detection, Machine learning, Artificial neural network

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