This study aims to investigate the impact of topic familiarity and vocabulary learning tasks on vocabulary acquisition in an ESP (English for Specific Purposes) setting, focusing on 16 adult learners with varying proficiency levels (A1-B1), all training to be cooks. Participants worked with six cooking recipes, three on familiar topics and three on unfamiliar topics, with 30 target words assigned to one of three tasks: word list, flashcards, or fill-in-the-gaps. Immediate and delayed post-tests (three weeks later) were administered using the Vocabulary Knowledge Scale (VKS). Despite the small sample size, significant differences in retention are noted across proficiency levels and task types over time. Results from repeated-measures ANOVAs showed that topic familiarity alone did not significantly impact vocabulary acquisition. However, the interaction between topic familiarity and task type was significant, as were the effects of task types and proficiency levels individually. The interaction between task type and proficiency level also showed a significant effect on vocabulary learning. Participants generally performed better with the fill-in-the-gaps method, but these effects decreased over time. The study highlights the importance of considering both proficiency levels and task types for effective vocabulary instruction.

This study used text mining to analyze public interest and research trends related to the Nakdong River. We examined news articles (1990~2024) and academic publications (1960~2024) to understand the evolving relationship between societal concerns and scientific research. Main findings reveal growing public interest towards the Nakdong River, with initial focus on pollution related to the industrial development shifting to large-scale river modification projects and water quality issues. This increased awareness mirrored trends in research, where early emphasis on classic ecology and water quality expanded to include sociocultural, educational aspects and advanced methodologies. The study highlights a dynamic interplay between public discourse and scientific inquiry, with research priorities aligning with societal concerns like water pollution, algal blooms, and biodiversity loss, underscoring the need for integrated, multidisciplinary research that addresses emerging environmental challenges and promotes sustainable management of the Nakdong River ecosystem under climate change and other societal shifts.

As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

본 연구는 키워드 네트워크 분석과 토픽모델링을 활용하여 국내 학술 지에 게재된 위기개입·상담 관련 연구동향을 분석하는 데 목적이 있다. 이를 위하여 국내 등재(후보) 학술지에 게재된 137편의 논문을 분석하였 다. 논문 분석을 위하여 넷마이너(NetMiner) 4.0 프로그램을 활용한 키 워드 네트워크 분석과 토픽모델링을 수행하였다. 키워드 네트워크 분석 을 통해 키워드 빈도분석, 키워드 동시출현빈도분석과 함께 연결중심성, 근접중심성, 매개중심성 등의 중심성 분석을 실시하였고, 토픽모델링분석 을 통해 논문에 잠재된 토픽과 키워드를 도출하였다. 키워드 네트워크 분석을 통한 키워드 빈도분석에서는 자살, 상담자, 전문가, 학교, 청소년 등이 주요 키워드로 나타났고, 토픽모델링 분석에서는 토픽-1(외상 스트 레스 위기상담), 토픽-2(취약계층 위기개입), 토픽-3(위기청소년 가족통합 서비스), 토픽-4(위기가정 개입), 토픽-5(자살집단 위기대응) 등 총 5개의 토픽이 등장하였다. 분석 결과를 토대로 위기개입·상담에 관한 연구는 다 양한 위기집단을 대상으로 위기 후 외상 스트레스에 대한 개입과 대응에 관한 연구들이 주로 이루어진 것을 확인하였다. 본 연구의 결과는 향후 위기개입·상담 연구를 위한 기초자료를 제공하고, 위기개입·상담에 필요 한 방안 마련에 기여할 것으로 보여진다.

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.

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

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.

The popularity of live streaming is driving the emergence of a new business model, known as live-streaming commerce (LSC). Consumers spend more and more time on smartphones, and the emerging business model of live-streaming commerce (LSC) is flourishing in the retail industry. With highly interactive features, LSC social interactions influence viewer purchase behaviors. To examine the interactions between influencers and viewers, we collected a rich dataset from a leading LSC service platform and integrated research models from the natural language process (NLP) field and econometric models.

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).

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.

본고에서는 한국어와 중국어 이중주어문에서 문장의 어순, 의미, 통사 그리고 서술 어의 ‘거리도상성(距離象似性)’ 등 몇 가지 방면을 모두 고려해야 주어와 화제를 잘 파악할 수 있다고 보았다. 주어와 화제에 관한 논쟁이 많이 일어나는 문장 유형 중, 성상형용사 구문, 소유-소재형용사 구문, 심리형용사 구문을 중심으로 검토하였는데, 화제는 독립된 통사 성분이 아니지만 통사 층위에서 실제적인 문법 특징을 가지고 있으며 어떤 성분이 화제 특징 [+TOP]을 가지게 하는 기능 용법인 것이다. 소위 한 국어의 이중주어문이라 하는 구문을 복문이 아니라 단문으로 분석해 내었다.