• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.2
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• pp.172-184
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• 2017

Nanotechnology is a leading branch of technology and is expected to improve national industrial competitiveness. For maintaining a sustainable growth in nanotechnology, Korean government has set up specific plans from a long-term perspective. One of these plans is tracking and promoting certain potential technologies called Future 30 Nanotechnologies. This study aims to develop an analysis framework for comprehending the Future 30 Nanotechnologies. We applied this framework to the carbon nanomaterials field. Through co-authorship and keyword network analysis, we identified the research trends of three countries (i.e., Korea, US, and China.). This research framework could be utilized in the development of a nanotechnology policy.

• Health Policy and Management
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• v.18 no.2
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• pp.86-106
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• 2008

This research analyzed. knowledge structure and its effect factor by analysis of co-author and keyword network in Korea's health policy and administration sector. The data was extracted from 339 articles listed in the Korean Journal of Health Policy and Administration, and was transformed into a co-author and keyword matrix. In this matrix the existence of a link was defined by impact factors which were calculated by the weight value of what the role was and the rate of how many authors contributed. We demonstrated that the research achievement was dependent on the author's status and network index. Analysis methods were neighborhood degree, correspondence analysis, multiple regression and the difference of weight distribution by research fields. Co-author networks were developed as closeness centrality as well as degree centrality by a few high productivity researchers. In particular, power law distribution was discovered in impact factor and research productivity. The effect of the author's role was significant in both the impact factor calculated by the participatory rate and the number of listed articles. Especially, this journal shared its major researchers who had a licensed physician with the Journal of Preventive Medicine and Public Health. Therefore, social scientists were likely to be small co-author network differently from natural scientists. It was so called 'two cultures' phenomenon. This study showed how can we verified academic research structure existed in the unit of journal like as citation networks. The co-author networks in the field of health policy and administration had more differentiated and clustered than preventive medicine and epidemiology fields.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1049-1054
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• 2018

Keyword searching used in the past as a method of finding similar patents, and automated classification by machine learning is using in recently. Keyword searching is a method of analyzing data that is formalized through data refinement. While the accuracy for short text is high, long one consisted of several words like as document that is not able to analyze the meaning contained in sentences. In semantic analysis level, the method of automatic classification is used to classify sentences composed of several words by unstructured data analysis. There was an attempt to find similar documents by combining the two methods. However, it have a problem in the algorithm w the methods of analysis are different ways to use simultaneous unstructured data and regular data. In this paper, we study the method of extracting keywords implied in the document and using the LDA(Latent Semantic Analysis) method to classify documents efficiently without human intervention and finding similar patents.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.69-80
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• 2018

In this study, we typified the project for revitalizing the rural area through text analysis using news data, and analyzed the main direction and characteristics of the project. In order to examine the factors emphasized among the issues related to the revitalization of rural areas, we used news data related to 'tourism' and 'livelihood', which are the main keyword of the project to promote rural areas. In the analysis, text mining techniques were used. Topic modeling was conducted on LDA techniques for major projects in 'tourism' and 'livelihood' keyword. Based on this, this study typified the projects that are carried out for the activation of rural areas by topic. As a result of the analysis, it was fount that the topics included in the project were distributed in 11 sub-types(Tourism Promotion, Regional Specialization, Local Festival, Development of Regional Scale, Urban and Rural Exchange, Agricultural Support, Community Forest Management, Improve the Settlement Environment, General Welfare Service, Low Class Support, Others). The characteristics of the rural revitalization projects were examined, and it was confirmed that domestic projects were carried out by tourism-oriented projects. To summarize, the government is making projects to revitalize rural areas through related ministries. Within the structure where the project is spreading to the region, a lot of projects are being carried out. It is understood that the tourism and welfare oriented projects are being carried out in the revitalization project of the domestic rural area. Therefore, in order to achieve the goal of rural revitalization, it is believed that it will be effective to carry out a balanced project to improve the settlement environment of the residents.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.103-109
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• 2023

The Safe Life is an essential subject of education introduced in the 2015 revised curriculum for first and second graders of elementary school to cultivate safety awareness. This study aims to explore the relationship between concepts and concepts that are important in the Safe Life textbook using a keyword network analysis. The results indicate that the areas of life safety, traffic safety, and disaster safety are emphasized in terms of topics in the textbook. The teaching and learning methods for the Safe Life education are mainly based on "experience" and "practice," and the most common teaching materials are card games and sticky stickers. Based on the findings, we suggest maintaining the experience-oriented safety education approach and diversifying the teaching and learning materials and methods to incorporate various safety education areas.

• 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 the Korean Dietetic Association
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• v.23 no.2
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• pp.192-201
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• 2017

This study compared the Center for Children's Foodservice Management in 2012, 2014, and 2016 using big data and opinion mining. The data on the Center for Children's Foodservice Management were collected from the portal site, Naver, from January 1 to December 31 in 2012, 2014, & 2016 and analyzed by keyword frequency analysis, influx route analysis of data, polarity analysis via opinion mining, and positive and negative keyword analysis by polarity analysis. The results showed that nursery had the highest rank every year and education supported by Center for Children's Foodservice Management has increased significantly. The influx of data has increased through the influx route analysis of data. Blog and $caf\acute{e}e$, which have a considerable amount of information by the mother should be helpful for use as public relations and participation recruitment paths. By polarity analysis using opinion mining, the positive image of the Center for Children's Foodservice Management was increased. Therefore, the Center for Children's Foodservice Management was well-suited to the purpose and the interests of the people has been increasing steadily. In the near future, the Center for Children's Foodservice Management is expected have good recognition if various programs to participate with family are developed and advertised.

• The Journal of Fisheries Business Administration
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• v.38 no.2
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• pp.1-24
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• 2007

The improvement and richness of research in a particular domain are fundamentally based on their own various research topics and scientific or systematic research methodology. Especially considering fisheries business administration as a branch of business administration, the interdisciplinary concept will be a important factor in the research of fisheries business administration. This study analyzes fisheries business administration research through bibliometric analysis and meta-analysis to examine meta-data including research trends, researcher characteristics, and keywords. The 225 source articles are all papers published from 1990 to 2006 in the Journal of Fisheries Business Administration Society of Korea. Comparing previous research from the 80s, the major areas of Korean fisheries business administration research have changed and the rate of co-researched papers has also increased. In keyword-based meta-analysis, the topics of recent research are well balanced and diversified but some structural and editorial problems still remain. The result from meta-data and bibliometric information gathered in this study will help create guidelines for carrying out further rigorous research and enhancing the quality of research in fisheries business administration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.543-546
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• 2017

Recently, people are using the data analysis in order to follow the new trend in information and communication technology. Media plays an important role to expand the new issue in our society, especially affected to establish social awareness about science and technology. So, We find some major technologies (Machine Learning & Blockchains) of future communication and information based on the 200 news articles through two data analysis methods such as keyword analysis and sentiment analysis. We look forward this paper to constantly develop the technology of information and communication as the guiding frame of the new scientific world.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.191-196
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• 2019

Humans use images on a daily basis-so much so that images are integral to their lives. Seeing is represented by an image, created or lived in it. Images required and developed a new paradigm from past to present. Today, images are in digital formats, and new techniques are increasing. Among them, cinemagraphs can find features that differ from previous images. The keywords found by comparing them in the image development are analyzed in detail through four characteristics in this paper. Cinemagraphs appearing in the keywords are interpreted in terms of each keyword and, through the example, the cinemagraph image can be approached concretely.