• Korea Trade Review
• /
• v.45 no.5
• /
• pp.239-260
• /
• 2020

This research was conducted on 176 GVCs-related research papers listed in the Index of Korean Academic Writers. The analysis methodology used the keyword network analysis methodology of big data analysis. For the comprehensive analysis of research trends, the research trends through word frequency (TF), important topic (TF-IDF), and topical modeling were analyzed in 176 papers. In addition, the research period of GVCs was divided into the early stages of the first study (2003-2014), the second phase of the study (2015-2017), and the third phase of the study (2018-2020). According to the comprehensive analysis, the GVCs research was conducted with the keyword 'value added' as the center, focusing on the keywords of export (trade), Korea, business, influence, and production. Major research topics were 'supporting corporate cooperation and capacity building' and 'comparative advantage with added value of overseas direct investment'. According to the analysis of major period-specific research trends, GVCs were studied in the early stages of the first phase of the study with global value chain trends and corporate production strategies. In the second research propulsion period, research was done in terms of trade value added. In the recent third phase of the study, small and medium-sized enterprises actively participated in the global value chain and actively researched ways to support the government. Through this study, the importance of the global value chain has been confirmed quantitatively and qualitatively, and it is recognized as an important factor to be considered in the strategy of enhancing industrial competitiveness and entering overseas markets. In particular, small and medium-sized companies' participation in the global value chain and support measures are being presented as important research topics in the future.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.10
• /
• pp.87-94
• /
• 2020

Social reviews such as SNS feeds and blog articles have been widely used to extract keywords reflecting opinions and complaints from users' perspective, and often include proper nouns or new words reflecting recent trends. In general, these words are not included in a dictionary, so conventional morphological analyzers may not detect and extract those words from the reviews properly. In addition, due to their high processing time, it is inadequate to provide analysis results in a timely manner. This paper presents a method for efficient keyword extraction from social reviews based on the notion of cohesion scoring. Cohesion scores can be calculated based on word frequencies, so keyword extraction can be performed without a dictionary when using it. On the other hand, their accuracy can be degraded when input data with poor spacing is given. Regarding this, an algorithm is presented which improves the existing cohesion scoring mechanism using the structure of a word tree. Our experiment results show that it took only 0.008 seconds to extract keywords from 1,000 reviews in the proposed method while resulting in 15.5% error ratio which is better than the existing morphological analyzers.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.20 no.2
• /
• pp.11-25
• /
• 2024

This study aims to analyze research trends in digital twin focusing on Germany, the US, and Korea. In Elsevier's Scopus, we collected 4,657 papers about digital twin published in from 2019 to 2023. Keyword frequency and centrality analysis were conducted on the abstracts of the collected papers. Through the obtained keyword frequencies, we tried to identify keywords with high frequency of occurrence and through centrality analysis, we tried to identify central research keywords for each country. In each country, 'digital_twin', 'machine_learning', and 'iot' appeared as research keywords with the highest interest. As a result of the centrality analysis, research on digital twin, simulation, cyber physical system, Internet of Things, artificial intelligence, and smart manufacturing was conducted as research with high centrality in each country. The implication for Korea is that research on virtual reality, digital transformation, reinforcement learning, industrial Internet of Things, robotics, and data analysis appears to have been conducted with low centrality, and intensive research in related areas appears to be necessary.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.758-764
• /
• 2018

Digital documents such as patents, scholarly papers and research reports have author keywords which summarize the topics of documents. Different documents are likely to describe the same topic if they share the same keywords. Document clustering aims at clustering documents to similar topics with an unsupervised learning method. However, it is difficult to apply to a large amount of documents event though the document clustering is utilized to in various data analysis due to computational complexity. In this case, we can cluster and connect massive documents using keywords efficiently. Existing bottom-up hierarchical clustering requires huge computation and time complexity for clustering a large number of keywords. This paper proposes an inverted index based bottom-up clustering for keywords and analyzes the results of clustering with massive keywords extracted from scholarly papers and research reports.

• Journal of Information Technology Applications and Management
• /
• v.29 no.3
• /
• pp.75-93
• /
• 2022

The purpose of this study is to compare and analyze each government's ICT SME support policies to cope with changes in the ICT ecosystem paradigm. In particular, the core policies and policy trends of the Moon's government are presented through keyword network analysis and cognitive map analysis. As a result, core technologies such as ICT(Information Communication Technology), AI(Artificial Intelligence), Big Data, and 5G, which have high values of betweenness centrality and closeness centrality, are major keywords with high propagation power. The cognitive map analysis shows that the opportunity factors for the 4th industrial revolution are being activated through the ICT infrastructure circulation process, the domestic market circulation process, and the global market circulation process. This study is meaningful in terms of cognitive map analysis and utilization based on scientific analysis.

• The Journal of Fisheries Business Administration
• /
• v.38 no.2
• /
• pp.1-24
• /
• 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
• /
• 2017.10a
• /
• pp.146-147
• /
• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.8
• /
• pp.55-63
• /
• 2021

Since social big data often includes new words or proper nouns, statistical morphological analysis methods have been widely used to process them properly which are based on the frequency of occurrence of each word. However, these methods do not properly recognize compound nouns, and thus have a problem in that the accuracy of keyword extraction is lowered. This paper presents a method to extract compound nouns in keyword analysis of social big data. The proposed method creates a candidate group of compound nouns by combining the words obtained through the morphological analysis step, and extracts compound nouns by examining their frequency of appearance in a given review. Two algorithms have been proposed according to the method of constructing the candidate group, and the performance of each algorithm is expressed and compared with formulas. The comparison result is verified through experiments on real data collected online, where the results also show that the proposed method is suitable for real-time processing.

• Asia-Pacific Journal of Business
• /
• v.14 no.3
• /
• pp.241-250
• /
• 2023

Purpose - The purpose of this study was to investigate the perception of 'unmanned cafes' on the network through big data analysis, and to identify the latest trends in rapidly changing consumer perception. Based on this, I would like to suggest that it can be used as basic data for the revitalization of unmanned cafes and differentiated marketing strategies. Design/methodology/approach - This study collected documents containing unmanned cafe keywords for about three years, and the data collected using text mining techniques were analyzed using methods such as keyword frequency analysis, centrality analysis, and keyword network analysis. Findings - First, the top 10 words with a high frequency of appearance were identified in the order of unmanned cafes, unmanned cafes, start-up, operation, coffee, time, coffee machine, franchise, and robot cafes. Second, visualization of the semantic network confirmed that the key keyword "unmanned cafe" was at the center of the keyword cluster. Research implications or Originality - Using big data to collect and analyze keywords with high web visibility, we tried to identify new issues or trends in unmanned cafe recognition, which consists of keywords related to start-ups, mainly deals with topics related to start-ups when unmanned cafes are mentioned on the network.

• Knowledge Management Research
• /
• v.22 no.1
• /
• pp.217-242
• /
• 2021

The purpose of this study is to examine the research trends in the filed of individual knowledge hiding through keyword network analysis. As individuals intentionally hide their knowledge beyond not sharing their knowledge in organizations and the research on knowledge hiding steadily spreads, it is necessary to examine the research trends regarding knowledge hiding behaviors. For keyword network analyses, we collected 346 kinds of 578 keywords from 120 articles associated with knowledge hiding behaviors. We also transformed the keywords to 86 nodes and 667 links by data standardizing criteria and finally analyzed the keyword network among them. Moreover, this study scrutinized knowledge hiding trends by comparing the conceptual model for knowledge hiding based on literature review and the network structure based on keyword network analysis. As results, first, the network centrality degree, knowledge sharing, creativity, and performance was higher than others in Degree, Betweenness, Closeness centrality. Second, this study analyzed ego networks about psychological ownership and individual emotion theoretically associated with knowledge hiding and explored the relationship between variables through comparing with the conceptual model for knowledge hiding. Finally, the study suggested theoretical and practical implications and provided the limitations and suggestions for future research based on study findings.