• Title/Summary/Keyword: 워드 클라우드 분석

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Keyword Analysis of COVID-19 in News Big Data : Focused on 4 Major Daily Newspapers

  • Kwon, Seong-Wook
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.12
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    • pp.101-107
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    • 2020
  • This paper aims to compare and analyze the major keywords according to the political orientation of progressive and conservative newspapers by utilizing the big data of the four major domestic daily newspapers related to COVID-19, which has entered a long-term war. To this end, 93,917 news reports from Jan. 20 to Sept. 15, 2020 were divided into four stages and the major keywords of the four newspapers were implemented and analyzed in WordCloud. According to the analysis, the conservative newspaper focused on the government's response, criticism, and China's responsibility by mentioning the keywords "government," "president," "state of affairs" and "mask" more than the progressive newspaper, while the progressive newspaper uses keywords that emphasize the seriousness of the disease and the occurrence of a dangerous situation. The Chosun Ilbo found that the use of various keywords during the massive outbreak of collective infections (2.18-5.15), and that the JoongAng Ilbo used keywords criticizing government policies in relation to reports of infectious diseases such as COVID-19, but also used keywords that emphasize the seriousness of diseases used by progressive newspapers and the occurrence of dangerous situations.

A study on the perception of 3D virtual fashion before and after COVID-19 using textmining

  • Cho, Hyun-Jin
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.12
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    • pp.111-119
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    • 2022
  • The purpose of this paper is to examine the change in perception of 3D virtual fashion before and after COVID-19 using big data analysis. The data collection period is from January 1, 2017, before the outbreak of COVID-19, to October 30, 2022, after the outbreak. Big data was collected for key words related to 3D virtual fashion extracted from social media such as Naver, Daum, Google, and YouTube using Textom. After the collected words were refined, word cloud, word frequency, connection centrality, network visualization, and CONCOR analysis were performed. As a result of extracting and analyzing 32,461 words with 3D virtual fashion as a keyword, the frequency and centrality of fashion, virtual, and technology appeared the highest, and the frequency of appearance of digital, design, clothing, utilization, and manufacturing was also high. Through this, it was found that 3D virtual fashion is being used throughout the industry along with the development of technology. In particular, the key words that stand out the most after COVID-19 are metaverse and 3D education, which are in high demand in the fashion industry.

Analysis of interest in non-face-to-face medical counseling of modern people in the medical industry (의료 산업에 있어 현대인의 비대면 의학 상담에 대한 관심도 분석 기법)

  • Kang, Yooseong;Park, Jong Hoon;Oh, Hayoung;Lee, Se Uk
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.11
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    • pp.1571-1576
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    • 2022
  • This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

Digital humanities Research Trends on Marcel Proust (마르셀 프루스트에 관한 디지털인문학적 연구 동향분석)

  • Jinyoung MIN
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.3
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    • pp.181-188
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    • 2024
  • Fueled by the digital transformation era, the 150th anniversary of Marcel Proust's birth (2021) and 100th anniversary of his death (2022) witnessed a surge in digital humanities research. This goes beyond supplementing traditional methods; it fosters new approaches like Nicolas Lagonneau's 'Proustonomics' website (archiving online/offline Proust discourse) and 'Proustographe' (quantifying and visualizing data related to Proust). The Buffalo Proust Project (2021) provided online access to materials on his life and works, while the Corr-Proust project digitized his correspondence. While Korea lacks established digital Proust research, recent analysis of academic paper vocabulary (through word frequencies and word clouds) reveals significant thematic and quantitative development around 2000, paving the way for future Korean ventures in this exciting field. Digital humanities research offers the potential to unearth new research topics, enhance efficiency, and promote international collaboration, ultimately leading to a deeper understanding of Proust and groundbreaking advancements in the field.

Classification of ratings in online reviews (온라인 리뷰에서 평점의 분류)

  • Choi, Dongjun;Choi, Hosik;Park, Changyi
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.845-854
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    • 2016
  • Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

Analysis of Pressure Ulcer Nursing Records with Artificial Intelligence-based Natural Language Processing (인공지능 기반 자연어처리를 적용한 욕창간호기록 분석)

  • Kim, Myoung Soo;Ryu, Jung-Mi
    • Journal of the Korea Convergence Society
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    • v.12 no.10
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    • pp.365-372
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    • 2021
  • The purpose of this study was to examine the statements characteristics of the pressure ulcer nursing record by natural langage processing and assess the prediction accuracy for each pressure ulcer stage. Nursing records related to pressure ulcer were analyzed using descriptive statistics, and word cloud generators (http://wordcloud.kr) were used to examine the characteristics of words in the pressure ulcer prevention nursing records. The accuracy ratio for the pressure ulcer stage was calculated using deep learning. As a result of the study, the second stage and the deep tissue injury suspected were 23.1% and 23.0%, respectively, and the most frequent key words were erythema, blisters, bark, area, and size. The stages with high prediction accuracy were in the order of stage 0, deep tissue injury suspected, and stage 2. These results suggest that it can be developed as a clinical decision support system available to practice for nurses at the pressure ulcer prevention care.

Comparative analysis on design key-word of the four major international fashion collections - focus on 2018 fashion collection - (4대 해외 패션 컬렉션의 디자인 key-word 비교분석 - 2018년 패션 컬렉션을 중심으로 -)

  • Kim, Sae-Bom;Lee, Eun-Suk
    • Journal of the Korea Fashion and Costume Design Association
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    • v.21 no.3
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    • pp.109-119
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    • 2019
  • The purpose of this study is to examine fashion trends and the direction of the four fashion collections by analyzing the design key-words of the four major international fashion collections in 2018. The data of this study was collected by extracting the key-words from Marie Claire Korea in 2018, with the total of the collected data numbering 2,144. The data was analyzed by text mining using the R program and word-cloud, and a co-occurrence network analysis was conducted. The results of this study are as follows: First, the key-words of fashion collection designs in 2018 were fringe and ruffle detail, silk and denim fabric, vivid color, stripe and check pattern, pants suit item, and oversized silhouette, focusing on romanticism and sport. Second, seasonal characteristics of the fashion collections were pastel colors in S/S, primary and vivid colors in F/W. Details were embroidery and cutouts in S/S, patchwork and fringe in F/W. Third, the design trends of the four major fashion collections were presented in the Paris collection: stripes, check patterns, embroidery, lace, tailoring, draping, romanticism, and glamor. In the Milan collection, checks, prints, denim, and minidresses reflected sport and romanticism. The London collection included fringe, ruffles, floral patterns, flower patterns, and romanticism. The New York collections included vivid colors, neon colors, pastel colors, oversize silhouettes, bodysuits, and long dresses.

Pre-primary early childhood teachers' perception of the subject of 'Infant Teaching and Learning Methods' in the Early Childhood Teacher Training Course (유아교원양성과정에서 '영유아 교수·학습방법' 교과목에 대한 예비유아교사의 인식)

  • Kwon, Jong Ae
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.5
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    • pp.423-429
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    • 2022
  • This study is a study on the perceptions of pre-primary early childhood teachers on teaching and learning methods for infants and toddlers in the early childhood teacher training process. This is a mixed study using word cloud analysis and qualitative case analysis on the subject, focusing on literature research and understanding of pre-primary early childhood teachers' 'teaching and learning methods for infants and toddlers'. The purpose of this study was to find out the meaning of a early childhood teacher through thoughts on teaching and learning methods for infants, difficulties, points to be learned, teaching competency to be good as a teacher, and experiences for teaching professionalism. Through the results of this study, it is expected to find a way to increase their sense of efficacy on teaching and learning methods when conducting classes for young children in the future, and to provide basic data for improving the quality of early childhood education.

Analysis of Research Trends in Home Economics Education by Language Network Analysis: Focused on the KCI Journals (2000-2019) (언어 네트워크 분석에 기반 한 가정과교육 연구 동향 분석: 2000-2019년 KCI 등재지를 중심으로)

  • Gham, Kyoung Won;Park, Mi Jeong
    • Journal of Korean Home Economics Education Association
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    • v.32 no.3
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    • pp.179-197
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    • 2020
  • This study analyzed the trends in home economics education research using the language network analysis method, focusing on papers published in the KCI list for 20 years from 2000 to 2019. A total of 501 home economics education papers analyzed through word cloud, centrality analysis, and topic modeling using NetMiner 4.4, and the results are as follows. First, the number of papers in home economics education published in the KCI listing increased gradually to 186 in the 2000s and 315 in the 2010s. The academic journals in which home economics education papers were published have been diversified to 16 in the 2000s and 22 in the 2010s. 60% of all papers were published in the 'Journal of Korean Home Economics Education Association', and since 2018, the number of papers published in the 'Journal of Learner-Centered Curriculum and Instruction' has increased dramatically. Second, in the 2000s and 2010s, home economics education studies published in KCI were categorized into home economics education content analysis, home economics educational program development & application, curriculum analysis, perception survey & direction exploration. In the 2000s, 'Home Economics Teacher' appeared as the main keyword, and a lot of perception survey & direction exploration were conducted. Relatively, the influence of 'development' increased in the 2010s, and many studies were conducted to analyze home economics education contents and develop and apply home economics programs. This study has significance in that it analyzed the research trend of HEE by expanding the analysis target and analysis period of the existing studies.

SNS Message as an Political PR Campaign Strategy: Focusing on the 21st General Election (정치 PR 전략으로서의 SNS 메시지 : 21대 총선을 중심으로)

  • Cha, Young-Ran
    • The Journal of the Korea Contents Association
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    • v.20 no.9
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    • pp.208-223
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    • 2020
  • In the 21st general election held in April 15, 2020, the importance of social media as an election campaigning tool became more prominent when engaged with Corona 19. Therefore, in this study, various studies were conducted to establish SNS strategy as an election campaign tool. This study analyzed the contents of SNS (Facebook, Twitter, YouTube) activities as an election campaign tool to analyze messages on social media messages of candidates Lee Nak-yeon and candidate Hwang Kyo-an of Jongno-gu, Seoul during the 2020 21st National Assembly election. Data collection mainly analyzed posts from each candidate's official account, and the research method used text analysis using the R program. Word cloud, comparative analysis, q-graph analysis, LDA, and STM analysis were used during text analysis. In addition, the analysis result was confirmed to be statistically significant through correlation analysis. As a result of research, candidate Lee Nak-yeon's election includes corona, people, problems, crisis, suffering, and wisdom, which indicates that the crisis caused by corona must be overcome through any means possible. On the other hand, candidate Hwang Kyo-an's election includes Moon Jae-in, the regime, save, the fatherland, the judge, and the economy. And from the perspective of political publicity, candidate Lee Nak-yeon made a lot of acclaims, while candidate Hwang Kyo-an made a lot of attacks, and both themes emphasized the policy rather than the image.