• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.549-566
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• 2022

Recently, artificial intelligence (AI) technology has progressed rapidly, and industries using this technology are significantly increasing. Further, analysis research using text mining, which is an application of artificial intelligence, is being actively developed in the field of social science research. About 125 laws, including joint laws, have been enacted under the Ministry of Oceans and Fisheries in various sectors including marine environment, fisheries, ships, fishing villages, ports, etc. Research on the laws under the Ministry of Oceans and Fisheries has been progressively conducted, and is steadily increasing quantitatively. In this study, the domestic research trends were analyzed through text mining, targeting the research papers related to laws of the Ministry of Oceans and Fisheries. As part of this research method, first, topic modeling which is a type of text mining was performed to identify potential topics. Second, co-occurrence network analysis was performed, focusing on the keywords in the research papers dealing with specific laws to derive the key themes covered. Finally, author network analysis was performed to explore social networks among authors. The results showed that key topics have been changed by period, and subjects were explored by targeting Ship Safety Law, Marine Environment Management Law, Fisheries Law, etc. Furthermore, in this study, core researchers were selected based on author network analysis, and the tendency for joint research performed by authors was identified. Through this study, changes in the topics for research related to the laws of the Ministry of Oceans and Fisheries were identified up to date, and it is expected that future research topics will be further diversified, and there will be growth of quantitative and qualitative research in the field of oceans and fisheries.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.95-106
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• 2021

The purpose of this study is to derive major topics in content R&D and provide directions for academic development by exploring research trends over the past 20 years using topic modeling targeting 9,858 papers published in the Journal of the Korean Contents Association. To secure the reliability and validity of the extracted topics, not only the quantitative evaluation technique but also the qualitative technique were applied step-by-step and repeated until a corpus of the level agreed upon by the researchers was generated, and detailed analysis procedures were presented accordingly. As a result of the analysis, 8 core topics were extracted. This shows that the Korean Contents Association is publishing convergence and complex research papers in various fields without limiting to a specific academic field. Also, before 2012, the proportion of topics in the field of engineering and technology appeared relatively high, while after 2012, the proportion of topics in the field of social sciences appeared relatively high. Specifically, the topic of 'social welfare' showed a fourfold increase in the second half compared to the first half. Through topic-specific trend analysis, we focused on the turning point in time at which the inflection point of the trend line appeared, explored the external variables that affected the research trend of the topic, and identified the relationship between the topic and the external variable. It is hoped that the results of this study can provide implications for active discussions in domestic content-related R&D and industrial fields.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.217-225
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• 2021

This study analyzed domestic research trends related to the behavior of problems with using smart devices in infants, focusing on addiction tendency. The main results of research trends are as follows: First, the total number of papers published per year is 79, and addiction proneness studies were published in various academic journals, while researchers' major was mostly concentrated in the department of education and social welfare. Second, there were 81 research topics, including actual conditions, scale development, related variables, interventions, and causal relationships. The proportion of research subjects was the highest with 60 (74.1%) research subjects, which are related variables. Third, the subjects of the study were divided into infants, caregivers, and teachers, and the main subjects of the study were mothers. Fourth, the research trends by research method were 73 quantitative studies (92.4%), 2 qualitative studies, 1 literature study, 2 mixed studies, and 1 other study. Fifth, the results of the analysis of influential factors were classified as risk factors and protection factors, which were specifically classified as individual factors, household factors, and social and environmental factors. Based on the study results, further discussion and practical suggestions were added.

• Korean small business review
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• v.42 no.3
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• pp.1-25
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• 2020

To commemorate the 40th anniversary of the foundation of the Korean Association of Small Business Studies, this study reviewed research papers on the subject of entrepreneurship in the Asia Pacific Journal of Small Business. For 40 years, the subjects of entrepreneurship-related studies published in this journal were relatively limited and the number of articles was very few. The research papers mainly focus on defining entrepreneurship as an innovation or as a determinant of innovation and survey-based empirical studies have been conducted since the publication of the Entrepreneurial Orientation(EO) by Lumpkin and Dess(1996). Although entrepreneurship is a research field that can be approached from various perspectives such as economics, sociology, psychology, cultural anthropology, and organization theory, most of the papers published in the Asia Pacific Journal of Small Business do not clearly state their theoretical positions or viewpoints. Moreover, there are few studies that incorporate corporate entrepreneurs or startup teams although they have been major actors or entrepreneurship. Lastly, innovation has been the main focus of research, leaving other arenas such as opportunity recognition and discovery understudied. In order to increase the quantity and improve the quality in the entrepreneurship research, we need to have the entrepreneurship field as one academic section in the Asia Pacific Journal of Small Business.

• Journal of Korean Home Economics Education Association
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• v.35 no.1
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• pp.53-72
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• 2023

This study aimed to develop educational material on the proper use of plant-based milk alternatives in the form of a card news, based on a focus group interview(FGI) with adult consumers. The FGI participants were individuals who had directly purchased cow's milk, soy milk, or other plant-based milk alternatives within the past three months and consumed them at least once a month. The study consisted of 17 adults between the ages of 19 and 63 years who met these criteria(9 males and 8 females). It was identified what participants were curious about, interested in, and needed information regarding the proper use of plant-based milk alternatives through FGI. A 10-page card news was developed that was highly usable, taking into account the FGI results and the latest literature. In conclusion, this study developed a card news aimed at promoting the proper use of plant-based milk alternatives, which can be easily disseminated online in line with the trends in food consumption and digitization. The results of this study suggest that the continuous development and distribution of educational materials that reflect food consumption trends and maximize their usability should be provided for dietary life education, such as school subjects or out-of-school programs.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.71-80
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• 2022

The purpose of this study is to analyze the social perception of invention education center using big data analysis method. For this purpose, data from January 2014 to September 2021 were collected using the Textom website as a keyword searched for 'invention+education+center' in blogs, cafes, and news channels of NAVER and DAUM website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by the Textom website, Ucinet 6, and Netdraw programs. The collected data were subjected to a primary and secondary refinement process and 60 keywords were selected based on the word frequency. The selected key words were converted into matrix data and analyzed by semantic network analysis. As a result of text mining analysis, it was confirmed that 'student', 'operation', 'Korea Invention Promotion Association', and 'Korean Intellectual Property Office' were the meaningful keywords. As a result of semantic network analysis, five clusters could be identified: 'educational operation', 'invention contest', 'education process and progress', 'recruitment and support for business', and 'supervision and selection institution'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to invention education center on the internet. The results of this study will be used as basic data that provides meaningful implications for researchers and policy makers studying for invention education.

• Trans-
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• v.14
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• pp.1-33
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• 2023

In the Web 3.0 era, the third generation of web technologies that uses blockchain technology to give creators ownership of data, metaverse is a crucial trend for developing a creator economy. Web 3.0 aims for a value in which content creators are compensated from participation without being dependent on the platform. Blockchain NFT technology is crucial in metaverse, a vital component of Web 3.0, to ensure the ownership of digital assets. Based on the theory that investigates the concept and characteristics of metaverse, this study identifies five features of the metaverse based NFT art ①'Continuity', ②'Presence', ③ 'Concurrency', ④'Economy', ⑤ 'Application of technology'. By focusing on metaverse based NFT art <Hybrid Nature> case study, we analyzed how the concepts and characteristics of the metaverse and NFT art were reflected in the work. This study focuses on the concept of NFT art, which is emerging at the intersection of art, technology and industry, and emphasizes the importance of finding creative, aesthetic, and cultural values rather than the NFT art's potential for financial gain. It is still in its early stage for academic studies to focus on the aesthetic qualities of NFT art. Future academics and researchers can find this study to gain deeper understanding of the traits and artistic, creative aspects of metaverse based NFT art.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.273-275
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• 2022

The use of drugs by pregnant women poses a potential risk to the fetus. Therefore, it is essential to classify drugs that pregnant women should prohibit. However, the fetal toxicity of most drugs has not been identified. This takes a lot of time and cost. In silico approaches, such as virtual screening, can identify compounds that may present a high risk to the fetus for a wide range of compounds at the low cost and time. We collected class information of each drug from the hazard classification lists for prescribing drugs in pregnancy by the government of Korea and Australia. Using the structural and chemical features of each drug, various machine learning models were constructed to predict fetal toxicity of drugs. For all models, the quantitative performance evaluation was performed. Based on the attention algorithm, important molecular substructures of compounds were identified in the process of predicting the fetal toxicity of the drug by the proposed model. From the results, we confirmed that drugs with a high risk of fetal toxicity can be predicted for a wide range of compounds by machine learning. This study can be used as a pre-screening tool for fetal toxicity predictions, as it provides key molecular substructures associated with the fetal toxicity of compounds.