• Journal of Digital Convergence
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• v.19 no.12
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• pp.43-50
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• 2021

This paper aims to identify trends of domestic industry-academic cooperation through comparative analysis of domestic and overseas academic articles published over the past 10 years (2011-2021). To this end, keyword network analysis and topic modeling analysis were performed to identify the characteristics of the entire articles collected. As results, it turned out that domestic articles included school, employment, education, patent, and professor as a major keyword while for overseas articles, project, policy, innovation, and company were the main topics, and related keywords were found to be influential. These results suggest that domestic industry-academic cooperation would have been designed and led by universities focusing on education for employment, and need to be carried out more actively in the areas of 'research' and 'technology transfer with the government's related policies and support on establishing two-way relationships that can benefit both schools and participating companies.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.55-63
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• 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
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• v.14 no.3
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• pp.241-250
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• 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.

• Journal of Korean society of Dental Hygiene
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• v.21 no.5
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• pp.639-644
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• 2021

Objectives: The purpose of this study was to analyze the factors that affected the number of citations for articles published in the Journal of Korean Society of Dental Hygiene based on previous studies. Methods: Information on papers including the number of citations was collected using a web crawling technique. The effect of the number of author keywords, the number of Medical Subject Headings (MeSH) keywords, MeSH match rate, abstract word count and keyword-abstract ratio on the number of citations was analyzed by multiple regression analysis. Results: The use of the MeSH keyword did not have a significant effect on the number of citations. Among the other factors, only the keyword-abstract ratio was statistically significant. Conclusions: Select a topic of constant interest in the field, write the title in detail using colons or asterisks if necessary, and do not repeat the words used in the title in keywords. Select specific keywords deeply related to the topic. In particular, choice words or phrases that are frequently used in the abstract. If the MeSH keyword selection contradicts the previous strategies, boldly give up the MeSH keyword.

• Journal of Software Engineering Society
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• v.29 no.2
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• pp.13-20
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• 2020

The exploratory test was introduced as a desirable test method due to its fast development cycle, but it is not actively adopted because documentation and analysis of the test range are required for application. On the other hand, keyword-based testing has been introduced as a way to save resources and facilitate maintenance, but it is difficult to plan tests in advance due to the large number of variables such as data, settings, interactions, sequence and timing. However, in keyword-based testing, you can create a test case based on keywords by presenting clear criteria and methods for creating keywords and applying the exploration testing process. In this paper, we propose a model that automates exploratory tests based on keywords. To verify the effectiveness, we compared the general keyword-based test(KBT) and keyword-based exploratory test(KBET), and compared with the exploratory normal test case(ETC) and keyword-based exploratory test(KBET).

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.177-178
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• 2019

본 연구는 지방교육자치의 가장 핵심인 '교육감' 관련 연구들의 주요 핵심어들과 그들 간의 관계성을 분석하였다. 본 연구에서는 2009년부터 2018년까지(10년간)의 '교육감' 관련 선행연구 총 93건을 키워드 네트워크 분석 방법론을 활용하여, 주요 핵심어 추출 및 워드 클라우드 제시, 주요 핵심어들 간의 관계성(의미망 네트워크) 분석 등을 진행하였다. 최근 10년간 국내 '교육감' 관련 연구들의 주요 핵심어들은 교육감선거, 주민직선제, 선출제도, 개선방안, 비교연구, 교육자치, 문제점, 지방자치, 교육부장관, 교육위원 등 이었다. 주요 핵심어들(상위 출현빈도)은 높은 밀도와 연결정도를 가지고 상호 네트워크를 형성하고 있었다. 본 연구결과는 향후 진행될 '교육감' 관련 후속연구들의 새로운 연구주제 선정 및 다양한 방향 설정에 기초자료로 활용될 수 있을 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.141-142
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• 2022

최근 빅데이터를 통해 여러 가지 분석을 진행하고 있다. 다만 이러한 방식으로는 키워드에 대해 여론에 대한 분석을 거치지 않아 정확한 분석이 힘들다는 문제점을 가지고 있다. 따라서 본 논문에서는 이러한 문제점의 개선을 위해 데이터를 수집하고 이에 대해 감정분석을 수행하는 컨테이너 기반의 시스템을 제안한다. 감정분석 시스템을 적용한다면 키워드에 대해 분석 시에 정확도가 더욱 높아질 것으로 전망된다.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.107-115
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• 2013

Various networks can be observed in the world. Knowledge networks which are closely related with technology and research are especially important because these networks help us understand how knowledge is produced. Therefore, many studies regarding knowledge networks have been conducted. The assortativity coefficient represents the tendency of connections between nodes having a similar property as figures. The relevant characteristics of the assortativity coefficient help us understand how corresponding technologies have evolved in the keyword-based patent network which is considered to be a knowledge network. The relationships of keywords in a knowledge network where a node is depicted as a keyword show the structure of the technology development process. In this paper, we suggest two hypotheses basedon the previous research indicating that there exist core nodes in the keyword network and we conduct assortativity analysis to verify the hypotheses. First, the patents network based on the keyword represents disassortativity over time. Through our assortativity analysis, it is confirmed that the knowledge network shows disassortativity as the network evolves. Second, as the keyword-based patents network becomes disassortavie, clustering coefficients become lower. As the result of this hypothesis, weconfirm the clustering coefficient also becomes lower as the assortative coefficient of the network gets lower. Another interesting result concerning the second hypothesis is that, when the knowledge network is disassorativie, the tendency of decreasing of the clustering coefficient is much higher than when the network is assortative.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.50-59
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• 2021

The main purpose of accident analysis is to identify the causal factors and the mechanisms of those factors leading to the accident. However, current accident analysis techniques focus only on finding the factors related to the accident without providing more insightful results, such as structures or mechanisms. For this reason, preventive actions for safety management are concentrated on the elimination of causal factors rather than blocking the connection or chain of accident processes. This greatly reduces the effectiveness of safety management in practice. In the present study, a technique to model the correlational structure of accident risk factors is proposed by using the co-occurrence keyword network analysis technique. To investigate the effectiveness of the proposed technique, a case study involving a portable ladder fall accident is conducted. The results indicate that the proposed technique can construct the correlational structure model of the risk factors of a portable ladder fall accident. This proves the effectiveness of the proposed technique in modeling the correlational structure of accident risk factors.

• Journal of Society of Preventive Korean Medicine
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• v.25 no.2
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• pp.33-43
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• 2021

Objectives : This study seeks to grasp the current status of Koryo medical research in North Korea, by focusing on researchers and research topics. Methods : A network analysis of co-authors and keyword which were extracted from Koryo Medicine - a North Korean traditional medicine journal, was conducted. Results : The results of author network analysis was a sparse network due to the low correlation between authors. The domain-wide network density of co-authors was 0.001, with a diameter of 14, average distance between nodes 4.029, and average binding coefficient 0.029. The results of the keyword network analysis showed the keyword "traditional medicine" had the strongest correlation weight of 228. Other keywords with high correlation weight was common acupuncture (84) and intradermal acupuncture(80). Conclusions : Although the co-authors of the Koryo Medicine did not have a high correlation with each other, they were able to identify key researchers considered important for each major sub-network. In addition, the keywords of the Koryo Medicine journals had a very high linkage to herbal medicines.