Keyword Frequency Analysis Research Articles

빅데이터 분석을 통해 본 놀이상담에 대한 사회적 인식

Objectives This study explored the current state of social perception of play counseling through big data analysis. This study tried to understand how play counseling is socially understood and what strategies are needed for the development of play counseling.
 Methods Texts such as various blogs, cafes, news, and web documents were collected from five platforms: Naver, Daum, Google, You Tube, and Twitter, with “play+counseling’ as the keyword. The analysis target was limited to the data of the last three years from January 1, 2020 to December 31, 2022. For data analysis, key word frequency analysis, TF-IDF analysis, semantic network analysis, and CONCOR analysis were applied.
 Results As the result of frequency analysis and TF-IDF analysis, ‘children’, ‘play’, ‘counseling’, ‘research’, ‘effect’, and ‘expert’ appeared as the top keywords of play counseling. As a result of semantic network analysis, the keyword with the strongest influence was ‘children’. As a result of the CONCOR analysis, the keywords for play counseling formed five clusters. These were named ‘goals and effects of play counseling’, ‘program elements of play counseling’, ‘certification of play psychological counselors’, ‘teacher training for play counseling’, and ‘elementary school’.
 Conclusions The perception of play counseling, which is mainly concentrated in elementary school, should be expanded to middle and high school students. Play counseling should be used to cultivate sociality and positive emotions in Korean children and adolescents.

텍스트 네트워크 분석을 활용한 오마카세와 이모카세에 대한 소비자 인식에 관한 연구

This study analyzed consumer awareness of 'Omakase' and 'Imokase', which have rapidly increased consumer interest in the last 2 to 3 years, using big data. Through this, it was intended to be used as basic data to interpret the meaning of consumer behavior for these two restaurant businesses. From January 1, 2022 to December 31, 2022, text data containing Omakase or Imokase were collected from Naver and Daum, and text mining, keyword frequency analysis, centrality analysis, semantic network analysis, and CONCOR analysis were performed. The results of this study are as follows. First, consumers have a positive perception of omakase and imokase. Second, Second, consumers perceive Omakase as follows. ① Region(Gangnam) ② Menu(sushi, Korean beef, sushi, Japanese food, beef) ③ How to use(dinner, lunch, course) ④ Important attributes(price, value for money, friends, use for the first time). Third, consumers perceive imokase as follows. ① region(Gangneung, Euljiro, Seomyeon) ② menu(yang-gobchang, sulsang, danji) ③ How to use(pub, anju) ④ important attribute (omakase, dongsang-imong, imo).

Research Trends in Liberal Arts Studies Before and After the COVID-19 Pandemic Using LDA Topic Modeling

This study analyzes the changing trends of research topics in liberal arts studies before and after the COVID-19 pandemic. It aims to investigate the impact of the pandemic on research trends in liberal arts studies, identify changes and new trends in recent years with the pandemic, and contribute to the search for the future direction of liberal arts studies in the post-COVID-19 era. To achieve these objectives, we applied keyword frequency analysis and LDA topic modeling analysis as text mining technologies to analyze the keywords and topics of research papers in liberal arts education before and after the pandemic. Our findings revealed meaningful differences in the topics and keywords of papers in liberal arts education studies before and after the COVID-19 pandemic. We interpret these results as reflecting the changes and directions of liberal arts education in the actual field. We also analyzed individual papers on extracted topics and keywords to more elaborately interpret the results and the meaning of the analyses. The results showed that new topics, such as COVID-19 and untact, emerged after the pandemic. The frequency of topics related to online education also increased after the pandemic. We also found an expansion of convergence and integration related to research topics in liberal arts education, as well as a considerable increase of interest in topics regarding artificial intelligence, literacy, and PBL after the pandemic. Based on the interests of researchers and trends in liberal arts education research, this study contributes to ongoing efforts to learn more about the impact of COVID-19 on the actual educational practices of liberal arts education. It is also significant in that it enables the observation and prediction of changes and trends in the research and actual fields of liberal arts education in the future.

텍스트 분석을 활용한 맞춤형 학업성취도 자율평가 관련 언론보도 분석

The purpose of this study is to analyze the perception of stakeholders and the public on the Customized Assessment of Educational Achievement (CAEA) proposed to ensure the basic level of academic performance of students in South Korea. For this purpose, news articles and online comments were collected from major newspapers and broadcasting stations using web-crawling method. The LDA topic modeling and semantic network analysis were carried out for the news articles, while keyword frequency analysis were carried out for the news articles and comments. Seven topics were extracted from the LDA analysis: (1) conflicting responses from superintendents for expansion of the CAEA, (2) conflicts between the Office of Education and teacher organizations over participation in the CAEA, (3) implementation of the comprehensive plan to ensure that every student meets the basic level of academic standards, (4) expansion of the CAEA for improving academic performance by conservative superintendents, (5) expansion of CAEA’s target grades, (6) the 2021 National Level Academic Achievement Assessment result, and (7) implementation of the CAEA and private education. In addition, the need for the national or state-level customized assessment and concerns about polarization in education associated with mandatory administration were found in the analysis of comments on news articles. Finally, based on the perceptions of stakeholders and the public explored through this study, implications for the educationally-sound applications of the CAEA were discussed.

Long-term Trends in Construction Engineering and Management Research in Korea

This bibliographic study presents a historical perspective on Korea's construction engineering and management (CEM) research and reports a 27-year analysis of papers written by Korean CEM researchers between 1993 and 2019. Frequency analysis of the collected papers was performed according to three main perspectives: the knowledge area, research topics, and methods. Knowledge area analysis shows that research fields have become more diversified and subdivided. Construction technology, cost management, and planning and schedule management have consistently maintained the top three positions for the past three decades. In addition, quality management, resource management, and risk management were also ranked high in the 2000s. In the 2010s, safety management, environmental management, quality management, and construction industry and business were ranked high. These changes reveal how Korean society's and industry's demands have changed over the years. Furthermore, the findings from keyword frequency analysis revealed that the focus of CEM research was mostly on cost and scheduling in the 1990s and on scheduling, risk, cost, and construction automation in the 2000s. The advent of Building Information Modeling (BIM) in the 2010s was an epochal change. This study found that the research methods frequently used by CEM researchers were simulation, optimization, regression analysis, computer vision and image processing, 3D modeling, case-based reasoning, genetic algorithm, case study, economic analysis, and artificial neural network.

Analysis of News Regarding on Shared Housing Using Text Mining Techniques

This study was conducted to explore the development direction of shared housing as a sustainable housing product by exploring the perception of shared housing and related words through Big data analysis and examining the correlation between keywords. Data was collected using Big Kinds, a news search service, and sentiment analysis, text mining analysis, N-gram and network analysis, and CONCOR analysis were performed using TEXTOM and UCINET, which are Big data analyses programs. As a result of the analysis. First, shared housing began to be introduced as a new housing type in late 2010 and began to be supplied in earnest in 2013. Second, the sensibility for shared housing has a liking and interest in the new space, but there is also a sense of rejection of living together. Third, as a result of keyword frequency analysis, the frequency of occurrence of related keywords indicating the residence of shared housing such as youth and single-person households and keywords indicating the characteristics of youth housing policy projects was high. Fourth, CONCOR analysis found significant four cluster characteristics: ‘Share house’, ‘Co-living’, ‘Remodeling social housing’, and ‘Young people rental housing’.

A bibliometric analysis of the literature on circular economy and sustainability in maritime studies.

Over the last decade, many academics, practitioners, and policy-makers have focused on the notion of circular economy (CE) as a way to operationalize sustainable development. There is, however, a shortage of review studies that reflect the evolution and status of CE with respect to sustainability in the maritime industry. The purpose of this study is to conduct a bibliometric analysis of the development of CE in research on sustainable maritime processes/operations in the maritime industry. To accomplish this, the study uses citation analysis (publication distribution, productive journals, cross-country collaborations), co-occurrence analysis of the subject categories, co-citation analysis, and keyword frequency analysis. To collect data, the study utilizes the Web of Science database. Such software packages as CiteSpace, Histcite, and VOSviewer are used to analyze the data collected. The results clarify that, in the maritime industry, CE is a micro-level sub-set of sustainability. Furthermore, the literature has mainly focused on waste management and life-cycle assessment as measures of CE 1.0 and CE 2.0, respectively. Assessing circularity in terms of R-imperatives highlights that "recycle," "remanufacture," "recover," and "reduce" are widespread practices of circularity in the maritime industry. Shipping companies should evaluate the possibility of implementing CE by realizing shorter loop R-imperatives such as "refuse," "reduce," "reuse," and "repair." Scholars must further explore underdeveloped R-imperatives such as "refuse," "refurbish," "repurpose," and "remine" in the maritime industry.

코로나시대 온라인수업에 대한 여대생의 인식 변화 탐색

COVID-19 has changed the paradigm of everyday life to online and has brought about many changes in university education. This study aims to identify changes in student perception of online classes using big data analysis. For this purpose, data from March 2020 to June 2021 were collected from the ‘Everytime’ community site, using Python programming language. Then text mining, namely, Keyword frequency analysis, network analysis, and sentiment analysis were conducted. The implications derived from the findings suggested some ideas worth considering in improving online classes at university.

A Study on Research Trends in 『Audiology and Speech Research』 Using Keyword Network Analysis

Purpose: The purpose of this study was to investigate the research trends of papers published in ‘Audiology and Speech Research’ and to obtain implications for the direction of research.Methods: Data were collected from 2005 to 2022 articles research information sharing service (RISS) which is provided by Korea education and Korea Citation Index (KCI). After extracting and cleansing keywords, the frequency, centrality, and visualization data analysis for keywords were performed using KrKwic & NodeXL programs.Results: According to the results of keyword frequency analysis, ‘hearing aid’ showed the highest frequency, followed by ‘hearing loss’, ‘noise’, and ‘cochlear implant’, etc. As a result of the keyword centrality analysis, it was found that the centrality was particularly high in ‘hearing aid’, ‘hearing loss’, ‘word recognition’, ‘cochlear implant’, ‘sensorineural hearing loss’, ‘auditory training’, ‘aural rehabilitaion’.Conclusion: The results of this study will provide useful data for understanding current research trends in ASR and further suggesting new research directions in the field of audiology and speech pathology.

Industry 4.0 research in the maritime industry: a bibliometric analysis

Industry 4.0 (I4.0) is currently a major topical concern in the literature on maritime industry. Maritime 4.0 is the area concerned with the integrated implementation of digital processes and technologies in the maritime industry. There is, however, a shortage of review studies that can reflect the research directions and various important issues in Maritime 4.0. The purpose of this study is to explore and categorize academic publications concerned with Maritime 4.0 to identify its current research status, main subject categories, emerging trends, current gaps, and future research directions. To provide a comprehensive review of the publications, the study used bibliometric analysis to conduct citation analysis (publication distribution, productive journals, and cross-country collaborations), cooccurrence of the subject categories, cocitation analysis, and keyword frequency analysis. In doing so, the study utilized the Web of Science (WoS) dataset and software packages for data analysis. Finally, the study proposed a research framework composed of five themes to link current research topics with future research trends in Maritime 4.0. The five themes observed were “digitalization technology,” “methodology and techniques,” “management,” “safety and security,” and “environmental issues.” The results revealed that, although scholars and practitioners focused on Maritime 4.0, this area remained underdeveloped. Many scopes under the five themes were left unexplored, such as emerging paradigms of Internet of things (IoT) and blockchain under the theme of “digitalization,” hybrid models (e.g., game theory and genetic algorithms) under the theme of “methodology and techniques,” and social responsibility under the theme of “environmental issues.”

텍스트마이닝을 활용한 유아 어머니 양육태도 연구의 동향

This study aims to examine the trends of research related to mothers' parenting attitudes by analyzing academic journal papers published with the keyword 'infant mother rearing attitude' from 2000 to 2022. The research target papers are 587 domestic academic papers collected using the database of the academic research information service. The collected data was analyzed using a network analysis method using big data. As a result of the study, first, as a result of examining the frequency analysis and connection centrality of major keywords in research papers from 2000 to 2022, studies related to mothers, infants, parenting attitudes, and influences were predominant. Connection centrality was highest in infants, followed by mother, parenting attitude, influence, relationship, sociality, temperament, and emotion. Second, as a result of modeling the main keyword topics, the themes appeared in the order of mother, infant, parenting attitude, influence, relationship, sociality, emotion, temperament, problem behavior, and mediating effect. Third, as a result of analysis by Concor network, it was classified into 7 groups. Based on this, it is meaningful to seek future research topics and directions related to infant mothers' parenting attitudes and to provide basic data related to parental education.

Foster Care Case Management and Child developmental Outcomes: Big Data Analysis for Case Management Records

연구목적: 본 연구의 목적은 가정위탁 사례관리의 현황과 그 성과를 파악하는 데 있다. 이를위하여 이전에 분석된 적이 없었던 가정위탁 사례관리기록의 내용을 분석하고 위탁아동, 위탁부모, 친부모에게 제공된 서비스의 양과 아동 발달성과 간의 관계를 파악하였다. 방법: 본 연구는 2019 가정위탁패널 1-2차년도 조사에 참여한 261명 아동에 대한 10개월 간의사례관리 기록(서비스제공일지)과 설문조사를 통해 측정된 그들의 발달성과를 분석하였다. 서비스제공일지 내용 분석을 위해서는 빅데이터 분석방법 중 키워드 빈도분석, 토픽모델링 분석등을 활용하였으며 서비스 제공 양과 아동 발달성과 간의 관계는 회귀분석을 통해 확인하였다. 결과: 서비스제공일지 분석 결과, 우선 위탁유형 별로 제공된 서비스 종류에 있어 큰 차이가없음을 알 수 있었다. 키워드분석 및 토픽모델링 분석 결과, 여성 양육자에 대한 키워드와 토픽이주되게 드러나 가정위탁의 주양육자는 여성이라고 인식된다는 점과 가정위탁 사례관리 기록이아동 중심보다는 양육자 중심으로 기술된다는 것을 파악할 수 있었다. 또한, 일반위탁의 경우친족위탁에서 나타나지 않던 ‘친가정서비스’, ‘자조모임’, ‘공적자원연계’ 등이 제공일지의 토픽으로 등장하였다. 각 서비스 종류의 양이 아동발달에 미친 영향을 보면, 전화상담이 많이 제공될수록 자아존중감, 우울·불안, 위축, 공격성에, 자립지원서비스는 위축에 각각 영향을 미치는것으로 나타났다. 결론: 본 연구 결과를 바탕으로 가정위탁 사례관리 개선 및 사례관리 기록 체계 개선방안에대하여 논하였다.Objectives: The purpose of this study was to explore the status of foster care case management and the relationship between case management and children's developmental outcomes. Methods: This study analyzed the contents of case management records of ten months of 261 foster care children who participated in the second wave survey of the 2019 foster care panel. In addition, the study merged data pertaining to the service provisions and survey results which measured children’s developmental outcomes. Results: As big data analysis, we used the keyword frequency analysis and topic modeling analysis methods. To analyze the relationship between service provision amounts and children's developmental outcomes, a partial correlation analysis was utilized. The results of the big data analysis were as follows. Firstly, there were no significant differences according to the types of foster care. Secondly, case management records showed that the main caregivers for children were women regardless of the type of foster care. Thirdly, the results of the topic modeling analysis revealed that ‘family home service’, ‘self-help groups’, and ‘links to public services’ were the main topics in terms of services provided only in non-kinship foster care groups. The results of the regression analysis showed that when more ‘telephone counseling’ and ‘services for independent living’ were provided, better outcomes in children's self-esteem, depression/anxiety, social withdrawal, and aggression could be expected. Conclusions: Based on these results, practice and policy implications were discussed to improve foster care case management.

A systematic literature review on humanitarian logistics using network analysis and topic modeling

The aim of this paper is to conduct a systematic literature review to understand the research trends, identify area of improvements and recommend the future research direction in humanitarian relief logistics (HRL). Using Netminer 4.0, keyword network analysis, keyword frequency analysis, and topic modeling analysis were undertaken. As a result, it has been identified that the number of studies on HRL continuously increased since it mounted in the mid-2010s, with the main issues changing by period. Keyword network analysis and keyword frequency analysis discovered that establishment of the HRL identity and disaster response, preparation and recovery were the main issues from 2005 to 2009. Whereas followed by education and practical logistics flow (2010 – 2014) and provision and coordination (2015 – 2019) to be the main focused area of research. Through topic modeling analysis, nine topics (preparation, coordination, stakeholders, sustainable development, facilities location, recovery, supplying and sourcing, emergent response, transportation, supplying and sourcing) were extracted. Facilities location was the most frequent topic that studied in the number of papers. Meanwhile, recovery, preparation, stakeholders, and emergent response showed downward trends but facilities location, coordination, and supplying and sourcing showing upward. This study is significant in the sense that it suggests research trends in different approach using keyword network analysis, keyword frequency analysis, network analysis and topic modeling which are one of the first attempts in research trend analysis in HRL field. This study will support HRL studies by understanding macroscopic and comprehensive way and set as a steppingstone for future research direction.

텍스트마이닝을 활용한 국내 메타버스의 연구현황 탐색

The purpose of this study is to explore the current state of research on metaverse in each academic field using text mining. For this purpose, the study analyzed 454 abstracts of journal articles dealing with metaverse according to each academic filed. Keyword frequency analysis, keyword network analysis, and topic modeling were used as text mining techniques. Results were as follows: First, 'education' was an important keyword based on the TF-IDF value regardless of academic fields, which indicates that metaverse is highly related with education in most academic fields. Second, 'technical' and 'virtual' were located at the center of keyword network in most academic fields, which indicates that these keywords play a mediating role among other keywords. However, 'education', which was an important keyword based on the TF-IDF value, was not high in keyword network. It means that while education is important that most academic fields concern, the relation degree with other keywords is not strong enough. Third, topic modeling shows that hot topic in each academic field was 'education using metaverse contents' in engineering, 'metaverse classroom' in social science, 'element of metaverse environment' in art and sports science, 'digital innovation in the world through metaverse' in interdisciplinary science, 'historical content through augmented reality elements' in humanities, 'teacher's perception of reality in metaverse' in pedagogy. The current state of research on metaverse identified are useful for interdisciplinary and convergence research to utilize metaverse in future education.

Text-mining analysis using national R&D project data of South Korea to investigate innovation in graphene environment technology

This study investigates the possibilities offered by the development of graphene environment technology that can contribute to new and effective approaches for the transition to an ecologically benign ecosystem. To analyze the research and development progress in graphene environment technology, graphene environment technology research trends of South Korea for the years 2009–2020 are investigated by acquiring information pertaining to national research and development projects related to graphene environment technology from the National Science and Technology Information Service. Both structured and unstructured data are analyzed using diverse text-mining methods, such as keyword frequency analysis, association rule mining, and topic modelling. The results indicate that graphene research in South Korea is focused primarily on graphene use in batteries and energy-storage devices, such as solar cells, fuel cells, and secondary batteries. This study can help understand the manner by which the South Korean government has been investing in the research and development of graphene environment technology; additionally, it discusses the future applications and prospects of graphene for the next decade.

Covid 19 시대 텍스트마이닝과 SNA 기법을 활용한 한국과 중국의 무역관계 분석

The objective of this study is to analyze international trade relation between Korea and China using text mining and SNA(Social Network Analysis) for the three Covid 19 years from 2019 to 2021. For the analysis, text mining analysis was conducted through keywords ‘China Export’ and ‘China Import’. And then, key issues were analyzed by using keyword frequency analysis. Finally, we analyzed centrality tests such as the degree centrality, eigenvector centrality, and the convergence of iteration correlation (CONCOR) using the social network analysis. As a result, Korea seems to show the most interest in internal and external trade risks in relation to Chinese imports and exports as it is highly dependent on China.

키워드 네트워크 분석을 활용한 유아 사회적 유능감 국내 연구 동향 분석

Objectives TThe purpose of this study is to identify research trends related to social competence of early childhood through keyword network analysis. Methods For a total of 440 social competence of early childhood-related papers published in Korea from 2000 to 2021, the frequency was calculated using the Excel 2016 program, and network centrality analysis was performed using the UCINET 6 program. In addition, frequency analysis was conducted based on the research method and research topic of the paper to be studied. Results First, the publication of social competence-related studies from 2000 to 2010 was low at the beginning, but the number increased explosively from 2011. Second, In both the first and second periods, Quantitative research was conducted the most in research related to social competence of young children, and research on the subject of exploring the relationship between related variables was conducted the most. Third, in the keyword frequency analysis, 2011 was divided into the first half and the second half. In the first half, young children’s temperament, mothers' parenting attitude, young children’s emotional intelligence, and in the second half, young children’s emotional intelligence, young children’s emotion regulation ability, young children’s self-regulation were the most frequent. Third, as a result of analyzing the centrality of the keyword network by dividing it into the first half and the second half based on 2011, the centrality of the connection was in the order of young children’s temperament, mothers' parenting attitude, young children’s emotional intelligence in the first half, mothers' parenting attitude, young children’s self-esteem, young children’s emotional intelligence in the second half. appear. Proximity centrality showed that in the first half of the year, young children’s temperament, mothers' parenting attitude, young children’s emotional intelligence, and in the latter period, young children’s self-esteem, teacher-child relationship, young children’s emotional intelligence were in that order. In the analysis of mediation centrality, young children’s temperament, young children’s emotional intelligence, mothers' parenting attitude in the first half were found in the order of young children’s self-esteem, teacher-child relationship, young children’s emotional intelligence in the latter period. In the prestige centrality analysis, young children’s temperament, mothers' parenting attitude, young children’s problem behavior problem behavior, mother's depression, and marital conflict were found in the first half of the year, followed by mothers' parenting attitude, young children’s self-regulation, and young children’s self-esteem in the later stages. Conclusions These results are significant in that they reviewed research trends related to the subject at a time when child abuse-related research is rapidly increasing, drew implications for future research directions.

외식산업 연구동향에 대한 탐색적 연구

The purpose of this study is to find a way to develop the food service industry through analysis of research trends related to the restaurant industry.
 In this study, through detailed search on RISS, 269 papers related to 'food service' with English abstracts were analyzed using R package to analyze keyword frequency, word clouding, keyword network analysis, LDA topic modeling, and TF, TF.-IDF verification was performed. The analysis results are as follows.
 First, as a result of keyword frequency analysis and word clouding, it was found that keywords related to food, service, restaurant, customer, intention, consumer, relationship, industry, satisfaction, company, etc. were frequently used.
 Second, as a result of word network analysis, service, restaurant, customer, and quality are connected centering on food. Management and performance, sns and type are separately connected. Brand, life, trust, menu, franchise, risk, etc. are being studied separately.
 Third, as a result of TF and TF-IDF analysis, the words in TF-IDF (eg, consultant, justice, blog, honbob, shop, globalization, leisure, coaching, complain, dakgalbi, label) in TF-IDF were not found in studies related to food service. It can be seen that related studies across various industries are also included.
 Finally, as a result of LDA topic modeling, the appropriate number of topics is 8, and each characteristic is defined as purchasing behavior, franchise, corporate strategy, workers, well-being, tourism, selection attributes, and environment. Topics 1 to 4 account for 74.2% of the total IDM.

Analysis of Text Mining Keyword Issues for Defense Drone on Social Network

Research on weapon systems has been conducted, and defense drones have been developed. However, few analyses that identify the needs of the general public have been conducted. The purpose of this study is to analyze the issues of interest in social networks regarding defense drones and extract keywords for activation. In this study, big data analysis tools such as Textom, Ucinet6, and NetDraw were used. The results of the keyword frequency analysis, centrality analysis, and CONCOR analysis showed that public information regarding social networks expressed keywords in defense drones, such as Defense Daily, News, Enterprise, Defense Science Research Institute, University, Hydrogen Battery, Army, Air Force, Urban Transportation, Fire, Regional, Enterprise, and Research. It is necessary to revitalize media publicity in developing cooperative technologies by companies, universities, the National Defense Research Institute, urban transportation, and firefighting, using army and air force drones, and developing future hydrogen batteries. In addition, issues of interest were observed for contents, such as applications in the United States, Israel, North Korea, Iran, and Afghanistan, and fostering university education. There is a need to present a case for the actual military use of defense drones and train the department of defense drones at universities.

빅데이터를 통해 살펴 본 유아교사 역량에 대한 사회적 인식 분석

Objectives The purpose of this study is to explore the direction of the competency of early childhood teachers by examining factors affecting the overall social perception and early childhood teacher competency of daycare center teacher competency, kindergarten teacher competency, and early childhood teacher competency through big data analysis. Methods The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. To this end, using the search terms for daycare center teacher competency, kindergarten teacher competency, and early childhood teacher competency, Naver blog, cafe, news, intellectual, academic information, web information and Daum blog, cafe, news, web information, Google news, Facebook and web documents were collected and analyzed. The collected data went through the first and second refining processes, and 50 major words were selected based on word frequency and semantic network analysis was performed. Results First, as a result of frequency analysis of keywords related to childcare center teacher competency, education showed the highest frequency, followed by childcare teachers, teachers, daycare centers, and support. As a result of centrality was education, and keyword with the highest mediation centrality was teacher. Second, as a result of frequency analysis of keywords related to kindertarten teacher compotencey, teachers showed the highest frequency, folowed by training, kindergarten, and education. As a result of centrality analysis, the keyworld with the highest connection centrality, proximity centrality, and mediation centrality was teacher. Third, as a result of frequency analysis of keywords related to the competency of early childhood teachers, education showed the highest frequency, followed by research, trainging, kindergarten, and teachers. As a result of the centrality analysis, the keyword with the highest connection centrality was pre-kindergarten teacher, and the keywrod with the highest proximity centrality and mediation centrality was research. Conclusions Through big data analysis, there was no difference in the perception of daycare center teachers and kindergarten teacher’s competency. It can be seen that the expectations for the role as a researcher are high.