This study analyzes the discourse of Korean internet users regarding patient clothing and identifies the changes to structure and content of clothing resulting from infectious disease outbreaks. The analysis draws on texts from Korean blogs, internet cafes, and news articles from 2011 to 2021 related to patient clothing. Using Ucinet 5 and NodeXL 1.0.1 programs, network density, centrality, and cluster analyses were conducted using the Wakita–Tsurumi algorithm. Additionally, Latent Dirichlet Allocation (LDA) topic modeling was applied using Python 3.7 to further explore thematic patterns within the discourse. Throughout the period of study, it was found that users consistently discussed the specific purpose and functionality of patient clothing. Following the outbreak of COVID-19, the distribution and influence of keywords related to the functional aspects of patient clothing, such as “hygiene and safety,” significantly increased. An increased focus was placed on elements such as functionality, activity, autonomy, hygiene, and safety during the pandemic as public health concerns grew. It can be seen that patients increasingly share their experiences online and hospitalization rates surge during health crises; this study provides valuable insights into how the design of patient clothing can be improved through various informatics techniques. It underscores the evolving perception of patient clothing as essential medical equipment during health emergencies. In addition, it offers practical guidance for enhancing designs that better reflect shifting societal concerns, particularly regarding health, safety, and patient comfort.

본 연구의 목적은 국내 학술지에 게재된 크리스천 코칭과 관련된 논문을 대 상으로 키워드 네트워크과 토픽을 분석하여 연구 동향을 살펴보는 것이다. 이를 위하여 KCI에서 2008년부터 2024년까지 한국연구재단 등재지와 등재후보지에 게재된 36건의 크리스천 코칭 관련 논문을 분석하였다. 키워드 네트워크와 토픽 모델링을 분석하기 위하여 넷마이너(NetMiner) 4.0 프로그램을 활용하였다. 키 워드 네트워크 분석은 빈도분석과 키워드 동시 출현분석, 중심성 분석(연결 중 심성, 근접 중심성, 매개 중심성)을 하였다. 토픽모델링 분석은 LDA 기법을 활 용하여 논문에 잠재된 토픽과 키워드를 추출하였다. 키워드 네트워크 분석 결과 ‘코칭’, ‘연구’, ‘크리스천’, ‘프로그램’, ‘교회’, ‘리더십’ 등이 주요 키워드로 나타났 다. 토픽모델링 분석 결과 Topic-1(상담활동), Topic-2(목회 활동), Topic-3(코 칭 활동), Topic-4 (크리스천 신앙), Topic-5(코칭 연구), Topic-6(연구 활동), Topic-7(교수 활동)으로, 총 7개의 토픽으로 구성되었다. 연구 결과 ‘코칭’, ‘연 구’, ‘교회’, ‘크리스천’ 등의 키워드가 높은 연결 중심성을 보였음이 확인되었다. ‘코칭’은 연결 중심성, 근접 중심성과 매개 중심성 모두에서 높은 값을 보여, 크 리스천 코칭 연구의 핵심적인 역할을 하고 있음이 나타났다. 본 연구의 결과는 크리스천 코칭 연구에 유용한 기초자료를 제공하고 크리스천과 교회 성장에 도 움을 줄 수 있는 방안 마련에 기여 할 것이다.

자율주행에 관한 관심은 전 세계적으로 증가하고 있으며, 글로벌 자동차 제조사들과 기술기업들이 자율주행 분야에 대한 투자를 늘 리고 있어 향후 자동차 산업과 교통체계 전반에 큰 변화가 전망된다. 이처럼 자율주행 관련 연구와 개발은 끊임없이 진보하고 있으며, 관련 연구 수행은 계속해서 이루어질 것으로 보인다. 연구 수행에 있어 동향 파악은 필수 요소이며, 본 연구에서는 국내 자율주행 연 구 동향을 분석하고자 한다. 연구 동향을 분석한 다양한 분야의 선행연구 검토 결과, 각각 연구 목적에 맞는 다양한 데이터베이스를 이용하여 데이터를 수집하였으며 연구 주제어 혹은 초록을 분석데이터로 활용하였음을 확인하였다. 자율주행 연구 동향에 대해 분석 한 선행연구 검토 결과, 기존 연구들은 분야를 구분하지 않고 연구를 수집·분석하였음을 확인하였다. 자율주행은 도로, 교통, 자동차, 기계, 컴퓨터, 전자, 전기 등 다양한 분야를 포함하고 있기에 분야별 연구 동향 분석이 필요하다. 이에 본 연구에서는 도로·교통 분야 의 동향 분석을 위해 최근 5년간(2019년~2023년) 국내 도로·교통 분야 등재 학술지에 게재된 학술 논문을 대상으로 연구 동향을 분석 하였으며, 보다 많은 텍스트 데이터를 활용하기 위해 주제어가 아닌 초록을 활용하였다. 키워드 출현 빈도 분석을 통해 주요 키워드를 도출하였으며, 토픽 모델링을 통해 주요 연구주제를 도출하였다. 본 연구에서 수행한 자율주행 연구 동향 파악은 도로·교통 분야에서 향후 수행될 자율주행 연구 방향 수립에 시사점을 제공할 것이라 기대된다.

The purpose of this study is to identify the major peacekeeping activities that the Korean armed forces has performed from the past to the present. To do this, we collected 692 press releases from the National Defense Daily over the past 20 years and performed topic modeling and social network analysis. As a result of topic modeling analysis, 112 major keywords and 8 topics were derived, and as a result of examining the Korean armed forces's peacekeeping activities based on the topics, 6 major activities and 2 related matters were identified. The six major activities were 'Northeast Asian defense cooperation', 'multinational force activities', 'civil operations', 'defense diplomacy', 'ceasefire monitoring group', and 'pro-Korean activities', and 'general troop deployment' related to troop deployment in general. Next, social network analysis was performed to examine the relationship between keywords and major keywords related to topic decision, and the keywords ‘overseas’, ‘dispatch’, and ‘high level’ were derived as key words in the network. This study is meaningful in that it first examined the topic of the Korean armed forces's peacekeeping activities over the past 20 years by applying big data techniques based on the National Defense Daily, an unstructured document. In addition, it is expected that the derived topics can be used as a basis for exploring the direction of development of Korea's peacekeeping activities in the future.

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting ‘topic over time’ graphs that can identify topic trends over time.

This study compared research trends in universities general English program before and after the COVID-19 pandemic. After analyzing 248 articles from KCI using frequency analysis, centrality analysis, and topic modeling, this study found consistent keywords indicating a focus on learning objectives, effectiveness analysis, satisfaction surveys, and level-based learning before and after the COVID-19 pandemic. Centrality analysis revealed keywords like “teaching, research, analysis” before COVID-19 and “satisfaction, study, level, activity, effect” after COVID-19, indicating a shift towards learner satisfaction, level-based learning, and effectiveness analysis due to the transition to online learning. Topic modeling revealed shifts in research trends: Pre-COVID-19 focused on effective teaching methods, evaluation techniques, and cultural content, while Post-COVID-19 prioritized online teaching methods, web-based platforms, and selfdirected learning. Future research should address self-directed learning, attitudes and goal setting, closing learning gaps in online/blended learning, and developing effective online assessment tools and evaluation strategies. This study provides valuable insights and directions for further research in general English programs.

본 연구에서는 코로나 이후 색조화장품 시장의 소비자들의 온라인 관심 정보에 대한 자료 수집 을 통하여 색조화장품 정보 검색의 특성과 텍스트 마이닝 분석 결과에 나타난 코로나 이후 색조화장품 시 장의 주요 관심정보들을 분석하고자 하였다. 실증분석에서는 “색조화장품” 이라는 단어를 포함하는 뉴스, 블로그, 카페, 웹페이지 등의 모든 문서들을 분석 대상으로 텍스트 마이닝을 수행하였다. 분석 결과 코로나 이후 색조화장품에 대한 온라인 정보 검색은 주로 구매 정보와 피부와 마스크 관련 화장법 등에 관한 정보 와 관심 브랜드와 행사 정보 등의 주요 토픽이 주를 이루고 있었다. 결과적으로 코로나 이후 색조화장품 구매자들은 적극적인 온라인 정보 검색을 통하여 제품 가치와 안전성, 가격 혜택, 매장 정보 등의 구매 정 보에 더욱 민감하게 될 것이므로 이에 대한 대응전략이 요구된다.

Non-fungible tokens (NFTs) exploded onto the global digital landscape in 2020, spurred by pandemic-related lockdowns and government stimulus (Ossinger, 2021). An NFT is a unit of data stored on a blockchain that represents or authenticates digital or physical items (Nadini, 2021). Since it resides on a blockchain, NFTs carry the benefits of decentralization, anti-tampering, and traceability (Joy et al., 2022). Fashion brands quickly capitalized on these features, launching fashion NFT collections and garnering significant profits from the sale of fashion NFTs in 2021 (Zhao, 2021). For example, Nike’s December 2021 acquisition of RTFKT (pronounced “artifact”) resulted in USD 185 million in sales less than a year after their acquisition (Marr, 2022).

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

This study aims to analyze research trends regarding outdoor wear. For this purpose, the data-collection period was limited to January 2002–October 2022, and the collection consisted of titles of papers, academic names, abstracts, and publication years from the Research Information Sharing Service (RISS). Frequency analysis was conducted on 227 papers in total to check academic journals and annual trends, and LDA topic-modeling analysis was conducted using 20,964 tokens. Data pre-processing was performed prior to topic-modeling analysis; after that, topic-modeling analysis, core topic derivation, and visualization were performed using a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: experiential marketing and lifestyle, property and evaluation of outdoor wear, design and patterns of outdoor wear, outdoor-wear purchase behavior, color, designs and materials of outdoor wear, promotional strategies for outdoor wear, purchase intention and satisfaction depending on the brand image of outdoor wear, differences in outdoor wear preferences by consumer group. The results of topic-modeling analysis revealed that the topic, which includes a study on the design and material of outdoor wear and the pattern of jackets related to the overall shape, was the highest at 30.9% of the total topics. The next highest topic was also the design and color of outdoor wear, indicating that design-related research was the main research topic in outdoor wear research. It is hoped that analyzing outdoor wear research will help comprehend the research conducted thus far and reveal future directions.

The forms and demands of language learning are changing in the pandemic era. Learners no longer rely solely on a formal language curriculum. Instead, they are using various informal language learning (ILL) channels. Although informal language learning has been in the spotlight and is growing, more research on ILL is needed. In this study, ILL taking place through an online community was analyzed. Articles from the Korean learning community (r/Korean in Reddit) were collected, and the topic modeling technique, Latent Dirichlet Allocation, was conducted on the collected data. As a result, seven major topics were selected. The most common topics in all posts were issues faced by beginner learners, followed by vocabulary and sentence meanings, interest in Chinese characters and applications of Korean language skills, culture and daily life, translation, online learning materials, and Korean phonics. Through this, the interests of ILL learners and the characteristics of learners could be identified. Due to the nature of ILL, in which a formal curriculum does not exist, it was found that questions about general strategies for learning and questions that could not be solved in formal language education were most prominent. In addition, the characteristics of ILL learners who actively sought learning content and materials were also found.

최근 전세계적으로 해양공간계획을 수립하고 공간활용 측면에서 다양한 용도를 포괄하고, 법제도화를 통해 공간관리를 추진하 고 있다. 또한 해양공간에서 발생되는 다양한 활동과 해양공간의 이용 범위와 강도가 확대되고 있는 가운데, 이해관계자 간 갈등 저감과 합리적인 공간관리수단으로써 해양공간계획의 중요성이 증대되고 있다. 이와 더불어 해양공간계획 관련 연구는 양적 성장과 다양한 연구 분야에서 수행되고 있다. 이 연구의 목적은 해양공간계획 관련 연구동향을 탐색하고 최근 10년간 연구주제의 변화와 이슈 키워드를 분석 하고자 한다. 연구대상은 2010년부터 2020년까지 해양공간계획을 핵심 주제어로 포함하는 연구문헌을 대상으로 키워드를 분석하였다. 분 석방법은 단어출현빈도, 워드 클라우드 등 출현강도를 기반으로 핵심 이슈를 발굴하고, 키워드를 중심으로 토픽과 연계된 5개 키워드를 추출하여 핵심 주제 도출하였다. 연구결과 정책수립 측면에서 정책수준단계(PRL)를 적용하여 원칙개발, 제도화, 정책검증 등 시기별 핵심 주제가 변화를 확인하였다. 국내연구는 의사결정도구로서 연구와 방법적용을 중심으로 수행되고 있으며, 향후 연구의 양적 성장과 질적 다변화를 통해 현재 시행초기의 해양공간계획이 실제 해양공간의 통합적 관리 및 조정 역할이 가능한 제도로의 정착을 기대한다.

Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.

As interest in the sustainable fashion industry continues to increase along with climate issues, it is necessary to identify research trends in sustainable fashion and seek new development directions. Therefore, this study aims to analyze research trends on sustainable fashion. For this purpose, related papers were collected from the KCI (Korean Citation Index) and Scopus, and 340 articles were used for the study. The collected data went through data transformation, data preprocessing, topic modeling analysis, core topic derivation, and visualization through a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: consumer clothing consumption behavior and environment, upcycle product development, product types by environmental approach, ESG business activities, materials and material development, process-based approach, lifestyle and consumer experience, and brand strategy. Topics were related to consumption, production, and education of sustainable fashion, respectively. KCI analysis results and Scopus analysis results derived eight topics but showed differences from the comprehensive analysis results. This study provides primary data for exploring various themes of sustainable fashion. It is significant in that the data were analyzed based on probability using a research method that excluded the subjective value of the researcher. It is recommended that follow-up studies be conducted to examine social trends.

The interest in text mining is recently increasing in the humanities and social sciences. Using a topic-modeling technique, this study analyzed a corpus of study abroad applications to explore a discursive field of study abroad. By doing so, this project finds the ways in which the new text analysis technique can contribute to the methodology of discourse analysis. For this purpose, 4,585 applications for a variety of undergraduate study-abroad programs were collected and sorted out into the corpora of successful and unsuccessful applications. The topic-modeling results show that generated topics generally match the discourses and themes that the existing research of study abroad have considered so far. The comparison of the results between successful and unsuccessful applications reveals that the former tends to exhibit a set of more clearly defined topics and use abstract and generalized words to describe actions engaging with study abroad. This study suggests that the topic-modeling technique can be a useful discourse-analytic tool as it helps understand a broad thematic and discursive terrain in a large size of textual data. This paper also discusses how traditional discourse analysis methods can contribute to addressing methodological limitations in text mining techniques.