• Journal of Korea Entertainment Industry Association
• /
• v.13 no.4
• /
• pp.139-147
• /
• 2019

This study conducted bibliometric analysis on studies of Korean intangible cultural heritage dance in the Seoul area and it aimed to figure out the tendencies of that research. For this, a list of Korean intangible cultural heritage dance studies of 24 events was collected and analysis was conducted through the big data analysis solution of TEXTOM. Text mining was used as the method for analysis. Research results showed that first, most of the studies were conducted on the Bongsan Talchum and studies on teaching and learning methods were especially actively conducted. On the other hand, there were not many studies on Gut and the need for research vitalization in that area was confirmed. Second, in studies on Cheoyongmu events, the term'contemporary Cheoyongmu' was used frequently. This can be considered the use of meaningful terms with regard to intangible cultural heritage dance that has changed throughout history. At this, the vitalization of research that can reveal the typicality of dance is demanded from research of other events as well. Third, there was a notable amount of research that compared and analyzed dance styles with regard to the Munmyoilmu. This was seen as the result of discussions in the Korean dancing world regarding archetypal dance styles expanding into academic discussions. Therefore, it was revealed that academic discussions can connect to academic outcomes apart from whether the matter is right or wrong.

• Journal of the Korean Society for information Management
• /
• v.40 no.2
• /
• pp.115-135
• /
• 2023

The purpose of this study is to assess the effectiveness of using deep learning language models to extract references automatically and create a reference database for research reports in an efficient manner. Unlike academic journals, research reports present difficulties in automatically extracting references due to variations in formatting across institutions. In this study, we addressed this issue by introducing the task of separating references from non-reference phrases, in addition to the commonly used metadata extraction task for reference extraction. The study employed datasets that included various types of references, such as those from research reports of a particular institution, academic journals, and a combination of academic journal references and non-reference texts. Two deep learning language models, namely RoBERTa+CRF and ChatGPT, were compared to evaluate their performance in automatic extraction. They were used to extract metadata, categorize data types, and separate original text. The research findings showed that the deep learning language models were highly effective, achieving maximum F1-scores of 95.41% for metadata extraction and 98.91% for categorization of data types and separation of the original text. These results provide valuable insights into the use of deep learning language models and different types of datasets for constructing reference databases for research reports including both reference and non-reference texts.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.63-88
• /
• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Journal of Technology Innovation
• /
• v.24 no.4
• /
• pp.249-275
• /
• 2016

For the past 50 years, innovation field has gone through an evolution. The range of research topics on innovation has expanded and diversified, along with a quantitative increase. In a multi-disciplinary field like innovation, to explore new topics and understand research trends, it is necessary to possess a comprehensive understanding regarding the current status of, and trends in, the research. In this study, the research trend in innovation studies from 2000 to 2015 was analyzed in a holistic perspective. For this, a novel technique, text mining was used. The result shows that innovation studies has focused on the traditional and emerging topics. Also, the differentiations has appeared in some of the traditional topics. This study provides not only an understanding of research dynamics, but also an opportunity to gain insights into the evolution of a new paradigm from an academic perspective.

• Journal of the Korean Society for information Management
• /
• v.28 no.4
• /
• pp.65-83
• /
• 2011

In this study, Network Text Analysis was performed on 1,752 articles which had been published in recent 7 years and drew the subject concept distribution and their relations in Library and Information Science research areas. Furthermore, for analyzing more recent trends and changing aspects, this study performed secondary analysis based on 482 articles published in recent 2 years. Results show that "public library", and "academic library" concepts were most frequently studied in the field and "evaluation", "education", and "web" concepts showed the highest-degree centrality during the recent 7 years. In the result of recent two years analysis, "web", and "classification" concepts showed high frequency and "user", and "public library" showed an improvement in high degree centrality.

• The Journal of the Korea Contents Association
• /
• v.8 no.11
• /
• pp.404-411
• /
• 2008

The purpose of this study is to suggest the effects of schema activation on reading comprehension. The subject of a sample survey was a 36 student experimental group and a 32 student control group, total 68 students at third grade class of C Middle School in Gwangju. Students ability to read English in the two groups were almost the same through, which was shown by pre-test administered before the beginning of the experiment. As a pre-reading activity, the experimental group was showed the pictures and vocabularies related to the text before reading. The other control group did Grammar Translation Method about text. The data needed for this study was obtained by the questionnaires with 25 questions about the English reading. The data analyzing method was t-test through the statistics program SPSS 12.0. The result of this study is as follows : First, the experimental group got a more meaningful score than the control group at the test. Second, pre-reading activities for providing prior knowledge of the text were affected by the student's English proficiency, peculiarly more effective on low level student than advanced level. Studying English reading through schema activation led the students to be present in classes with interests, so the experimental group showed more academic accomplishments than the control group.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.2
• /
• pp.231-235
• /
• 2008

Recently we witnessed a few scandals on plagiarism among academic paper and novels. Plagiarism on documents is getting worse more frequently. Although plagiarism on English had been studied so long time, we hardly find the systematic and complete studies on plagiarisms in Korean documents. Since the linguistic features of Korean are quite different from those of English, we cannot apply the English-based method to Korean documents directly. In this paper, we propose a new plagiarism detecting method for Korean, and we throughly tested our algorithm with one benchmark Korean text corpus. The proposed method is based on "k-mer" and "local alignment" which locates the region of plagiarized document pairs fast and accurately. Using a Korean corpus which contains more than 10 million words, we establish a probability model (or local alignment score (random similarity by chance). The experiment has shown that our system was quite successful to detect the plagiarized documents.

• Journal of the Korea Convergence Society
• /
• v.12 no.9
• /
• pp.295-305
• /
• 2021

The purpose of this study is to explore the trend of nursing professional research topics published domestically through quantitative content analysis. The research method performed procedures for collecting academic papers, refining and extracting words, and data analysis. A text network was developed by collecting 351 papers and extracting words from the abstract, and network analysis and topic modeling were performed. The core-topics were nurses, nursing professionalism, nursing students, nursing care, professional self-concept, health care professionals, satisfaction, clinical competence, and self-efficacy. Through topic modeling, topic groups of nurse's professionalism, nursing students' professionalism, nursing professional identity, and nursing competency were identified. Over time, core-topics remained unchanged, but topics such as role conflict and ethical values in the 1990s, self-leadership and socialization in the 2000s, and clinical practice stress and support systems in the 2010s have emerged. In conclusion, it is necessary to facilitate multidimensional interventional research to improve nursing professionalism of clinical nurses and nursing students.

• Journal of Korean Society of Forest Science
• /
• v.110 no.3
• /
• pp.308-321
• /
• 2021

The purpose of this study was to analyze domestic research trends over the past 20 years and future direction of forest ecology using text mining. A total of 1,015 academic papers and keywords data related to forest ecology were collected by the "Research and Information Service Section" and analyzed using big data analysis programs, such as Textom and UCINET. From the results of word frequency and N-gram analyses, we found domestic studies on forest ecology rapidly increased since 2011. The most common research topic was "species diversity" over the past 20 years and "climate change" became a major topic since 2011. Based on CONCOR analysis, study subjects were grouped intoeight categories, such as "species diversity," "environmental policy," "climate change," "management," "plant taxonomy," "habitat suitability index," "vascular plants," and "recreation and welfare." Consequently, species diversity and climate change will remain important topics in the future and diversifying and expanding domestic research topics following global research trendsis necessary.

• Journal of Korean Society for Quality Management
• /
• v.49 no.4
• /
• pp.551-567
• /
• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.