• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.33-63
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• 2018

The purpose of this study is to identify trends and to analyze knowledge structure and knowledge flow of studies on science and technology policy adopting the keyword network analysis. For analysis, we established a data set of published papers in the Journal of Korea Technology Innovation Society and Journal of Technology Innovation from 1993 to 2016. As a result of analysis, a great part of existing researches have been carried out in Seoul and Daejeon area, universities and public research institute. So it should be carried out in diversified form rather than being concentrated on specific regions or research performing subjects in the future. By research scope, more than 70% of research were carried out in 'R&D management' and 'technology innovation'. In the future, more research must be carried out in 'public process of science and technology' and 'public management of science and technology'. On the other hand, we performed the keyword network analysis to identify the knowledge structure of studies on science and technology policy. As a result of analysis, it was clustered into 'public process of science and technology', 'public management of science and technology', 'R&D management' and 'technology innovation', So It has not been different from research scope proposed by Yi, Kim and Oh(2016). And also, in earlier stage, research topics were general such as researchers and R&D investment. But as time goes by, topics were more diversified and subdivided. Therefore, in future, the research scope of studies on science and technology policy should be further expanded in terms of 'public process of science and technology', 'public management of science and technology', 'R&D management' and 'technology innovation', and systematic knowledge structure should be formed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.183-191
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• 2020

The purpose of this study is to objectively analyze the trend of media articles related to the 'high school credit system' (2017~2019: 3 years), which has become the biggest concern among Korean education policies, through BIGKinds, a news data big data analysis service for media companies. The main research methodologies were BIGKinds system's specific search term news search, news trend analysis, keyword extraction and wordcloud implementation, network analysis and network picture presentation. The research results are as follows; First, the number of articles related to the high school credit system that appeared in major media outlets in Korea for 3 years from 2017 to 2019 was 3,649. The number of articles was sharply increased at a certain point about 4 times, based on the government's announcement of related policies. It showed an increasing news trend. Second, the top 20 keywords that emerged from the press articles related to the high school credit system for 3 years of analysis were presented, and it was confirmed that the keyword change by year appeared. Third, the network of media articles related to the high school credit system was visualized and presented in different ways by person, institution, and keyword. The results of this study confirmed that the high school credit system education policy was adopted as the representative education policy of the Moon Jae-in government, and is proceeding in the policy decision stage and policy implementation stage.

• Journal of Korean Library and Information Science Society
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• v.50 no.3
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• pp.139-166
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• 2019

The purpose of this study is to study digital divide research trends of LIS fields and other social science fields through the analysis of author keyword network of peer-reviewed journal articles using NetMiner4 software. The author keyword was collected from KCI database. The results of the study were as follows: 1) the digital divide studies were focused on information services provision for information disadvantaged group by the public libraries in LIS fields. However, the studies of other social science fields were focused on the unique characteristics of information society and a new phenomenon of digital divide in the smart era, 2) compared with the other social science fields, there were a few researches about the old among the underprivileged, 3)there was little interest in other types of libraries except public libraries in LIS fields, 4)there is a need to study new types of digital divide in the smart era by LIS scholars.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.468-488
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• 2018

The maker movement is a phenomenon of society and culture where people who make necessary things come together and share knowledge and experience through creativity. However, as the maker movement has grown rapidly over the past decade, there is still a lack of consensus for how far they will be viewed as a maker movement. We need to look at how the maker movement has changed so far in order to find the direction of development of the maker movement. This study analyzes the media articles using text-based big data analysis methodology to understand how the issue of the maker movement has changed in general media. In particular, we apply Keyword Network Analysis and DTM(Dynamic Topic Model) to analyze changes of interest according to time. The Keyword Network Analysis derives major keywords at the word level in order to analyze the evolution of the maker movement, and DTM helps to identify changes in interest in different areas of the maker movement at three levels: word, topic, and document. As a result, we identified major topics such as start-ups, makerspaces, and maker education, and the major keywords have changed from 3D printer and enterprise to education.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.439-450
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• 2019

The purpose of the study was to critically explore the academic identity of dance education as a paradigm of practice-based education. Dance education is recognized as a school dance since the first curriculum was designed, which was announced by the Ministry of Education in 1955. Although Korea's dance education has 65 years of history, its academic identity as a dance education is not very strong. Traditional dance education was teacher-centered, skills-oriented, and exercise-oriented by following the practice-based paradigm. Recently, an initiative was taken to establish a new paradigm for dance education in schools, communities, and professional fields. This study followed the keyword network analysis and reviewed the main contents of each section of dance education research from a time-series perspective. The first section (1968-1979) is a practice of dance education based on physical education; the second section (1980-1989) is a creative-based movement education for primary education; the third section (1990-1999) is a systematization of dance education courses by class; the fourth section (2000-2009) is a paradigm for cultural and artistic education; the fifth section (2010-2019) consisted of various educational practices and institutions. Based on the research results, efforts are requested to establish an academic identity that can support dance education, interdisciplinary practice, and research.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.7
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• pp.101-108
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• 2019

In this research, I conducted a semantic network analysis centering on media articles on purchasing, revitalizing, and utilizing the former Busan branch of Hansung Bank, a modern architectural heritage. We sought the most efficient analysis elements for the analysis of the social arguments about preservation and utilization embedded in media articles. For this reason, Degree Centrality measures how many connections the word described in the media article has, and Betweenness Centrality measures the influence that controls the flow of information through correlation I examined. In addition, keyword that express the theme well examined the aggregation structure in each sub-network. In this research, in theoretical terms, it makes sense in that the social discussion embedded in the article of the mass media is grasped empirically through semantic network analysis of words. Methodological aspect is best when it includes nouns and adjectives and the distance between words is more than four words in the analysis of the cohesive structure of the semantic network to determine whether the influence of social discussions is best assessed through the connection between words to media articles.

• Journal of Korean Library and Information Science Society
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• v.51 no.2
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• pp.1-21
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• 2020

The purpose of this study is to explore semantic characteristics of IFLA school library guidelines through network analysis. There are two versions, 2002 edition and 2015 revision of the guidelines. This study analyzed the 2002 edition and 2015 revision of the IFLA school library guidelines view point of semantic network, and compared characteristics of two versions. The keywords were to extracted from two texts, semantic network were composed based on co-occurrence relations with keywords. The centrality(degree centrality, closeness centrality, betweenness centrality) was analyzed from the network. In addition, this study conducted topic modeling analysis using LDA function of NetMiner4.0. The result of this study is following these. First, When comparing the centrality, the 'Program, Teaching, Reading, Inquiry, Literacy, Media' keyword was higher in the 2015 revision than in the 2002 edition. Second, 'Inquiry' in degree centrality and 'Achievement' in closeness centrality which were not included in the 2002 edition top-ranked keyword list, have new appeared in 2015 revision. third, As a result of the analysis of topic modeling, compared to the 2002 version, the importance of topics on programs and services, teaching and learning activities of librarian teacher, and media and information literacy is increasing in the 2015 revision.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.119-134
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• 2018

A knowledge map as the taxonomy used in a knowledge repository should be structured to support and supplement knowledge activities of users who sequentially inquire and select knowledge for problem solving. The conventional knowledge map with a hierarchical structure has the advantage of systematically sorting out types and status of the knowledge to be managed, however it is not only irrelevant to knowledge user's process of cognition and utilization, but also incapable of supporting user's activity of querying and extracting knowledge. This study suggests a methodology for constructing a networked knowledge map that can support and reinforce the referential navigation, searching and selecting related and chained knowledge in term of contents, between knowledge. Regarding a keyword as the semantic information between knowledge, this research's networked knowledge map can be constructed by aggregating each set of knowledge links in an automated manner. Since a keyword has the meaning of representing contents of a document, documents with common keywords have a similarity in content, and therefore the keyword-based document networks plays the role of a map expressing interactions between related knowledge. In order to examine the feasibility of the proposed methodology, 50 research papers were randomly selected, and an exemplified networked knowledge map between them with content relevance was implemented using common keywords.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.187-192
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• 2023

In this study, trends in ICT education were investigated by analyzing the frequency of appearance of keywords related to machine learning and using conversion of iteration correction(CONCOR) techniques. A total of 304 papers from 2018 to the present published in registered sites were searched on Google Scalar using "ICT education" as the keyword, and 60 papers pertaining to ICT education were selected based on a systematic literature review. Subsequently, keywords were extracted based on the title and summary of the paper. For word frequency and indicator data, 49 keywords with high appearance frequency were extracted by analyzing frequency, via the term frequency-inverse document frequency technique in natural language processing, and words with simultaneous appearance frequency. The relationship degree was verified by analyzing the connection structure and centrality of the connection degree between words, and a cluster composed of words with similarity was derived via CONCOR analysis. First, "education," "research," "result," "utilization," and "analysis" were analyzed as main keywords. Second, by analyzing an N-GRAM network graph with "education" as the keyword, "curriculum" and "utilization" were shown to exhibit the highest correlation level. Third, by conducting a cluster analysis with "education" as the keyword, five groups were formed: "curriculum," "programming," "student," "improvement," and "information." These results indicate that practical research necessary for ICT education can be conducted by analyzing ICT education trends and identifying trends.