소셜 빅데이터 기반 신기술 분야 산학협력 트렌드 분석
An Analysis of Industry-Academic Cooperation Trends in New Technology Based on Big Data
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(Purpose) In this study, social big data related to industry-academic cooperation in the new technology field was collected, the current status and network of industry-academic cooperation in the new technology field were analyzed, and major topics were derived to identify their characteristics and trends. (Design/methodology/approach) For the study, TEXTOM was used to collect social data related to industry-academic cooperation in new technology fields from 2021 to 2022 in major portal sites such as Google, Naver, and Daum. The collected data derived words purified through the text mining process, and based on this, frequency analysis and centrality analysis, N-gram analysis, matrix analysis, and topic analysis were performed. (Findings) As a result of word frequency analysis, industry-academic cooperation in the new technology field is currently showing the highest interest in the artificial intelligence field, and TF-IDF analysis shows that the future automobile field is a promising new technology field. It was found that industry-academic cooperation activities in the field of new technologies were carried out in the form of business agreements, education, and support projects. As a result of the matrix analysis, it was found that the future automobile and artificial intelligence fields are relatively more closely connected than other fields, and the activation patterns of each industry-academic cooperation activity in each field are different. In addition, through the results of topic analysis, industry-academic cooperation in IT such as artificial intelligence and big data seems to be the most active, and these activities are closely related to college entrance, education, and employment. In the semiconductor field, discussions on infrastructure construction, ecosystem creation, and reinforcement are being held, and activities are being conducted focusing on cooperation between companies and human resource development to lay the foundation. (Research implications or Originality) These research results can be used as useful data for predicting industry-academic cooperation activities in the new technology field due to the relationship between industrial-academic cooperation activities in the new technology field and to find ways to support industry-academic cooperation activities in the future.
Abstract
(Purpose) In this study, social big data related to industry-academic cooperation in the new technology field was collected, the current status and network of industry-academic cooperation in the new technology field were analyzed, and major topics were derived to identify their characteristics and trends. (Design/methodology/approach) For the study, TEXTOM was used to collect social data related to industry-academic cooperation in new technology fields from 2021 to 2022 in major portal sites such as Google, Naver, and Daum. The collected data derived words purified through the text mining process, and based on this, frequency analysis and centrality analysis, N-gram analysis, matrix analysis, and topic analysis were performed. (Findings) As a result of word frequency analysis, industry-academic cooperation in the new technology field is currently showing the highest interest in the artificial intelligence field, and TF-IDF analysis shows that the future automobile field is a promising new technology field. It was found that industry-academic cooperation activities in the field of new technologies were carried out in the form of business agreements, education, and support projects. As a result of the matrix analysis, it was found that the future automobile and artificial intelligence fields are relatively more closely connected than other fields, and the activation patterns of each industry-academic cooperation activity in each field are different. In addition, through the results of topic analysis, industry-academic cooperation in IT such as artificial intelligence and big data seems to be the most active, and these activities are closely related to college entrance, education, and employment. In the semiconductor field, discussions on infrastructure construction, ecosystem creation, and reinforcement are being held, and activities are being conducted focusing on cooperation between companies and human resource development to lay the foundation. (Research implications or Originality) These research results can be used as useful data for predicting industry-academic cooperation activities in the new technology field due to the relationship between industrial-academic cooperation activities in the new technology field and to find ways to support industry-academic cooperation activities in the future.
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