• Journal of the Korean Society of Radiology
• /
• v.16 no.3
• /
• pp.343-349
• /
• 2022

We intend to use topic modeling to identify radiation-themed papers published from 1989 to 2022 and analyze the relevance and weight between topics. This study analyzed topics derived from national subjects for 717 papers published until recently in 2022 to contribute to the revitalization of research in the field of radiation. Through text mining, overall research trends on the subject distribution of the study were analyzed, and five topics were derived through topic modeling. First, among the papers to be analyzed, a total of 1,675 words were frequency-analyzed through the preprocessing process of key words in a total of 717 papers centered on keywords. Second, as a result of analyzing topics based on the association of constituent words for five topics, it was found that studies focused on minimizing dose in the range that does not degrade image quality in the fields of radiation, image, CT clinical. In addition, it was found that various studies were mainly conducted in the MRI, and the study of ultrasound in various areas of disease analysis was actively attempted.

• Journal of Creative Information Culture
• /
• v.6 no.1
• /
• pp.23-30
• /
• 2020

This study used the text mining method to analyze the research trend of the Journal of Creative Information Culture(JCIC) which is the journal of convergence. The existing research trend analysis method has a limitation in that the researcher's personality is reflected using the traditional content analysis method. In order to complement the limitations of existing research trend analysis, this study used topic modeling. The English abstract of the paper was analyzed from 2015 to 2019 of the JCIC. As a result, the word that appeared most in the JCIC was "education," and eight research topics were drawn. The derived subjects were analyzed by educational subject, educational evaluation, learner's competence, software education and maker culture, information education and computer education, future education, creativity, teaching and learning methods. This study is meaningful in that it analyzes the research trend of the JCIC using text mining.

• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.785-792
• /
• 2021

Since the Fourth Industrial Revolution, various changes have occurred in society as a whole due to advance in technologies such as artificial intelligence and big data. The amount of data that can be collect in the process of applying important technologies tends to increase rapidly. Especially in academia, existing generated literature data is analyzed in order to grasp research trends, and analysis of these literature organizes the research flow and organizes some research methodologies and themes, or by grasping the subjects that are currently being talked about in academia, we are making a lot of contributions to setting the direction of future research. However, it is difficult to access whether data collection is necessary for the analysis of document data without the expertise of ordinary programs. In this paper, propose a text mining-based topic modeling Web application model. Even if you lack specialized knowledge about data analysis methods through the proposed model, you can perform various tasks such as collecting, storing, and text-analyzing research papers, and researchers can analyze previous research and research trends. It is expect that the time and effort required for data analysis can be reduce order to understand.

• The Korean Journal of Applied Statistics
• /
• v.35 no.6
• /
• pp.739-754
• /
• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.1
• /
• pp.463-469
• /
• 2023

The aim of this study is to provide basic data that can contribute to improving successful clinical adaptation and reducing turnover of new graduate nurses by analyzing research related to reality shock experienced by new graduate nurses using text network analysis. The topics of reality shock experienced by new graduate nurses were extracted from 115 papers published in domestic and foreign journals from January 2002 to December 2021. Articles were retrieved from 6 databases (Korean DB: DBpia, KISS, RISS /International DB: Web of science, Springer, Scopus). Keywords were extracted from the abstract and organized using semantic morphemes. Network analysis and topic modeling for subject knowledge structure analysis were performed using NetMiner 4.5.0 program. The core keywords included 'new graduate nurses', 'reality shock', 'transition', 'student nurse', 'experience', 'practice', 'work environment', 'role', 'care' and 'education'. In recent articles on reality shock experienced by new graduate nurses, three major topics were extracted by LDA (Latent Dirichlet Allocation) techniques: 'turnover', 'work environment', 'experience of transition'. Based on this research, the necessity of interventional research that can effectively reduce the reality shock experienced by new graduate nurses and successfully help clinical adaptation is suggested.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.378-378
• /
• 2022

상수도시스템의 과정 중 최종 단계인 급수단계에서 지역전반에 수질문제가 발생할 경우, 직간접적인 피해의 해결은 장기간 지속될 수 있다. 본 연구에서는 실시간 비정형정보의 빅데이터 분석을 통해 상수도시스템에서 수질사고 문제의 파급력과 2차 피해 등의 연결 관계 변화 추적을 위한 기초적 분석을 수행하였다. 과거 대규모 수질사고가 발생된 바 있는 인천광역시 유충발생 사고를 대상으로 뉴스 기사 웹크롤링 절차를 정립하고, 그 결과를 분석하였다. '인천 유충'이 최초 보도되었던 2020년 7월 13일 부터 이후 1년을 대상으로 네이버 통합검색에 의해 표출되는 뉴스기사를 웹크롤링하였으며, 프로그래밍을 통한 불용어 제거 및 관련성 검토를 통해 총 920건의 기사를 분석하였다. 수질사고의 전개양상에 따라 사고발생, 확산, 수습, 그리고 보상의 4단계로 임의 구분하여 분석하였다. 의미분석을 위한 토픽모델링 기법은 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA) 방법을 적용하였으며, 긍부정 감정분석은 KNU 한국어 감성사전(KNU sentiment lexicon)을 활용하여 수행하였다. 토픽 모델링 결과, 사고 발생에서부터 확산, 수습, 보상의 단계에 맞춰 적절한 주제어의 조합에 따른 기사들이 도출되었으며, 단계별 긍부정 기사 비율역시 사고의 전개단계에 따라 적절히 나타남을 확인하였다. 제시된 수질사고 관련 비정형정보 분석 방법론과 결과는 과거 사고 사례 분석을 통한 검색 및 긍부정 키워드 확정, 키워드 발생 비율 변동(사고전과 후)에 따른 상황판단 기준설정 등에 활용이 가능하다.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.503-512
• /
• 2024

This study examines the essays of academic background, written by students from a university, which is classified into prestigious universities in Korean society. By Latent Dirichlet Allocation, 172 essays were analyzed to explore the students' perspectives of the academic fractionalism. The analysis identified five topics such as, functional aspects (Topic 1), double-edged nature (Topic 2), power communities (Topic 3), symbols of victory (Topic 4), and dysfunctional aspects (Topic 5). The most frequently appearing keywords are 'individual,' 'status,' and 'means' in Topic 1, 'definition,' 'school,' and 'meaning' in Topic 2, 'people,' 'origin,' and 'power' in Topic 3, 'university,' 'ability,' and 'effort' in Topic 4, and 'academic achievement,' 'South Korea,' and 'origin' in Topic 5. By exploring the topics, we found that students regarded class reproduction by education as important social issues and they showed little interest in other factors influencing academic fractionalism, such as race or ethnicity. these findings suggest that professars, who teach the impact of education on academic fractionalism, deal with the influence of diverse factors on academic fractionalism.

• Journal of Digital Convergence
• /
• v.18 no.6
• /
• pp.9-16
• /
• 2020

The purpose of this study is to understand the public needs for AI education actively promoted and supported by the current government. In doing so, 11 metropolitan news articles and Twitter posts regarding AI education that have been posted from January 1, 2018 to December 31, 2019 were collected. Then, word frequency analysis using TF(Term Frequency) method and LDA(Latent Dirichlet Allocation) method of topic modeling analysis were conducted. The topics of the news articles turn out to be a macroscopic policy support such as 'training female manpower in the AI field' and 'curriculum reform of university and K-12', whereas the topics of twitter delineate more detailed social perception on future society, such as future competencies and pedagogical methods, including 'coexistence with intelligent robots', 'coding education', and 'humane education competence development'. The findings are expected to be used to suggest the implications for the composition and management of AI curriculum as well as the basic framework of human resources development in the future industry.

• Journal of Broadcast Engineering
• /
• v.17 no.2
• /
• pp.270-283
• /
• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.

• Economic and Environmental Geology
• /
• v.54 no.3
• /
• pp.353-364
• /
• 2021

Since the mid-twentieth century, geology has gradually evolved as an interdisciplinary context in South Korea. The journal of Economic and Environmental Geology (EEG) has a long history of over 52 years and published interdisciplinary articles based on geology. In this study, we performed a literature review using topic modeling based on Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to identify geological topics, historical trends (classic topics and emerging topics), and association by analyzing titles, keywords, and abstracts of 2,571 publications in EEG during 1968-2020. The results showed that 8 topics ('petrology and geochemistry', 'hydrology and hydrogeology', 'economic geology', 'volcanology', 'soil contaminant and remediation', 'general and structural geology', 'geophysics and geophysical exploration', and 'clay mineral') were identified in the EEG. Before 1994, classic topics ('economic geology', 'volcanology', and 'general and structure geology') were dominant research trends. After 1994, emerging topics ('hydrology and hydrogeology', 'soil contaminant and remediation', 'clay mineral') have arisen, and its portion has gradually increased. The result of association analysis showed that EEG tends to be more comprehensive based on 'economic geology'. Our results provide understanding of how geological research topics branch out and merge with other fields using a useful literature review tool for geological research in South Korea.