• The Korean Journal of Applied Statistics
• /
• v.29 no.6
• /
• pp.1007-1023
• /
• 2016

This paper explores the conditions and potential of newly designed and tried methodology of big data analysis that apply to Korean history subject matter. In order to advance them, we need to pay more attention to quantitative analysis methodologies over pre-existing qualitative analysis. To obtain our new challenge, I propose 'digital history' methods along with associated disciplines such as linguistics and computer science, data science and statistics, and visualization techniques. As one example, I apply interdisciplinary convergence approaches to the principle and mechanism of elite reproduction during the Korean medieval age. I propose how to compensate for a lack of historical material by applying a semi-supervised learning method, how to create a database that utilizes text-mining techniques, how to analyze quantitative data with statistical methods, and how to indicate analytical outcomes with intuitive visualization.

• The Korean Journal of Applied Statistics
• /
• v.34 no.2
• /
• pp.267-278
• /
• 2021

As time goes by, the amount of data is increasing regardless of government, business, domestic or overseas. Accordingly, research on big data is increasing in academia. Statistics is one of the major disciplines of big data research, and it will be interesting to understand the research trend of statistics through big data in the growing number of papers in statistics. In this study, we analyzed what studies are being conducted through abstract data of statistical papers in Korea and abroad. Research trends in domestic and international were analyzed through the frequency of keyword data of the papers, and the relationship between the keywords was visualized through the Word Embedding method. In addition to the keywords selected by the authors, words that are importantly used in statistical papers selected through Textrank were also visualized. Lastly, 10 topics were investigated by applying the LDA technique to the abstract data. Through the analysis of each topic, we investigated which research topics are frequently studied and which words are used importantly.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.12
• /
• pp.83-91
• /
• 2020

This study aims to identify research topics and examine the trend of Covid19-related papers on DBpia. Applying latent Dirichlet allocation (LDA), we have extracted seven research topics, each of which concerns "International Dynamics", "Technology & Security", "Psychological Impact", "Biomedical-Related", "Economic Impact", "Online Education", and "Religion-Related". In addition, we used the multinomial logistic model to examine the trend of research topics. We found that the papers mainly cover topics related to "International Dynamics" and "Biomedical-Related" before June 2020, but the topics have become diverse since then. In particular, topics regarding "Economic Impact", "Online Education" and "Psychological Impact" has drawn increased attention of researchers. The findings would provide a guideline for collaboration in Covid19-related research, and could serve as a reference work for active research.

• The Journal of the Korea Contents Association
• /
• v.9 no.11
• /
• pp.175-183
• /
• 2009

This study is the full-scale research of a TV drama writer, who has been out of scholarly pursuits, examining the differentiality and tendency of the most popular TV drama writer, Su-Hyeon Kim. By focusing on her recent melodrama , this study shows that the writer used her own category grammar, 'pursuit of psychology' and 'reversal of dichotomy', which led her to convey the drama's message of the 'self-reflection' on love successfully. This analysis would be the good result of overcoming all the raised melodrama's negative elements in Koran TV such as conventionality, dichotomy, unreality, and excessive emotion. Also this paper presents that the writer showed an advanced tendency on the gender ideology, overthrowing the existing patriarchal gender ideology. This study proposes the further research to analyze what sort of influence is the writer's own category grammar. Also this study proposes the following research on that the writer's advanced tendency in melodrama could applicate the other genre drama of her's, stressing the necessity of sustaining research work on TV drama writer.

• Journal of Elementary Mathematics Education in Korea
• /
• v.23 no.4
• /
• pp.459-479
• /
• 2019

The purpose of this study is to explore the research trends and knowledge structures of 『Journal of Elementary Mathematics Education in Korea』 by applying the keyword network analysis. To do this, we analyzed the frequency of the occurrence of keywords in the journal and conducted keyword network analysis using the Krkwic program and NodeXL program. The results of the analysis are as follows. Firstly, 749 keywords were extracted from keyword cleansing process and 48 keywords, including mathematics curriculum, mathematics textbooks, school mathematics, mathematical problem solving, mathematically gifted student, etc. appeared more than five times. Secondly, the keyword network analysis showed that the keywords-mathematics textbooks, school mathematics, mathematical problem solving, mathematical communications-have high connection centrality. Finally, we provided the limitations of this study and suggested future research.

• Journal of Digital Convergence
• /
• v.15 no.9
• /
• pp.231-239
• /
• 2017

Solar energy is attracting attention as an alternative to fossil fuels. However, there was a lack of discussion on the overall research direction and future direction of research in technology development. In order to develop more effective technology, we analyzed and discussed the technology trend of solar energy using patent data and thesis data. As an analysis method, topics were selected by using topic modeling and text mining, the increase of included keywords was analyzed, and the direction of development of solar technology was analyzed. Research on solar power generation technology is expected to proceed steadily, and it is analyzed that intensive research will be done especially on high efficiency and high performance technology. Future studies could be conducted by adding overseas patent data and various paper data.

• Journal of Convergence for Information Technology
• /
• v.7 no.6
• /
• pp.53-59
• /
• 2017

This study was attempted to identify the domestic research related to depression and stress. The subjects of the analysis were 1,875 college degree theses thrown in the National Assembly Library searched by the depression and stress keyword as of November 30, 2016. The analysis method visualizes atypical data with Word Cloud, which is one of the text mining techniques. We also used the R'LDA package and LDA to classify treatment and subjects. As a result of the analysis, 233(12.4%) of the total papers with therapeutic keywords were found. Application of treatment methods was art therapy, music therapy, horticultural therapy, cognitive behavior therapy, clinical art therapy, cognitive therapy, psychological therapy, depression treatment, group therapy, laughter treatment sequence. The study subjects were adolescents, elderly, patient, mother, child, female, parents, and college students in order. The results of LDA topic analysis for adolescents were classified into four topics: self-support, treatment program, relationship effect, and variable study.

• The Journal of Society for e-Business Studies
• /
• v.23 no.4
• /
• pp.41-62
• /
• 2018

Despite the rapid growth of the mobile shopping market, major market participants are continuing to suffer operating losses due to severe competition. To solve this problem, the mobile shopping market requires research to improve customer satisfaction and customer loyalty rather than excessive competition. However, the existing studies have limits to reflect the direct needs of customers because they extract the factors on the basis of the Technology Acceptance Model and the literature study. In this study, to reflect the direct requirements of users of mobile shopping Apps, we derived concretely and various factors influencing customer satisfaction through a topic analysis using user reviews. And then we assessed the importance of derived factors to customer satisfaction and analyzed the effects of customer satisfaction on customer complaints and customer loyalty on a structural equation model based on the American customer satisfaction index. We expect that our framework linking a topic analysis and a structural equation model is to be applicable to studies on the customer satisfaction of other mobile services.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.5 no.5
• /
• pp.59-68
• /
• 2015

Since interdisciplinary convergence could beyond the borders of each disciplines, it is able to create new and meaningful knowledge through collaborative research between different study areas. Especially, in recent years, the Digital Humanities has attracted the attention as the convergence form of the Humanities and ICT. From a research methodology perspective, the Digital Humanities is a tool that can be used as a convergence system for various information utilization such as storage, retrieve, share, and spread. In view of Information system, Digital Humanities has been constructed and used in a variety of systems. Among them, studies related to information visualization for the Digital Humanities have been actively conducted. To visualize data or information, various types such as images, multimedia, interface, and etc could be applied. In this paper, we analyze the cases of various information visualization in digital humanities systems, and propose a method to utilize them in the Humanities and Area Studies.

• Annual Conference on Human and Language Technology
• /
• 2020.10a
• /
• pp.304-309
• /
• 2020

본 연구에서는 한국어 전자사전 DECO(Dictionnaire Electronique du COreen)와 다단어(Multi-Word Expressions: MWE) 개체명을 부분 패턴으로 기술하는 부분문법그래프(Local-Grammar Graph: LGG) 프레임에 기반하여 반자동으로 개체명주석 코퍼스 DecoNAC을 구축한 후, 이를 개체명 분석에 활용하고 또한 기계학습에 필요한 도메인별 학습 데이터로 활용하는 DecoNERO 개체명인식 플랫폼을 소개하는 데에 목적을 두었다. 최근 들어 좋은 성과를 보이는 것으로 보고되고 있는 기계학습 방법론들은 다양한 도메인을 기반으로한 대규모의 학습데이터를 필요로 한다. 본 연구에서는 정교하게 설계된 개체명 사전과 다단어 개체명 시퀀스에 대한 언어자원을 바탕으로 하는 반자동으로 학습데이터를 생성하는 방법론을 제안하였다. 본 연구에서 제안된 개체명주석 코퍼스 DecoNAC 기반 접근법의 성능을 실험하기 위해 온라인 뉴스 기사 텍스트를 바탕으로 실험을 진행하였다. 이 실험에서 DecoNAC을 적용한 경우, KoBERT 모델만으로 개체명을 인식한 결과에 비해 약 7.49%의 성능향상을 기대할 수 있음을 확인하였다.