• Title/Summary/Keyword: 워드클라우드 분석

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Analysis of press articles related to 'high school credit system' using BIGKinds system (빅카인즈(BIGKinds) 시스템을 활용한 '고교학점제' 관련 언론기사 분석)

  • Kwon, Choong-Hoon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2020.01a
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    • pp.99-100
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    • 2020
  • 본 연구는 최근 우리나라 국민들의 주요 관심 교육정책인 '고교학점제' 관련 언론기사들을 한국언론재단의 빅카인즈(BIGKinds) 시스템을 활용하여 분석하였다. 본 연구에서는 2018년 1월 1일부터 2019년 11월 30일까지 기간을 설정한 후, 총 54개 언론사의 '고교학점제' 관련기사들을 추출하였다. 그 다음, 추출된 '고교학점제' 관련 기사들을 대상으로 뉴스트렌드 분석, 네트워크 지도 구현, 핵심어 추출 및 워드클라우드 제시 등의 연구과정을 거쳤다. 본 연구결과는 '고교학점제'의 정책 진행 과정성의 과제 및 쟁점들을 해결하는데 기초자료로 활용될 수 있을 것으로 기대된다.

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Research Trend Analysis on Smart healthcare by using Topic Modeling and Ego Network Analysis (토픽모델링과 에고 네트워크 분석을 활용한 스마트 헬스케어 연구동향 분석)

  • Yoon, Jee-Eun;Suh, Chang-Jin
    • Journal of Digital Contents Society
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    • v.19 no.5
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    • pp.981-993
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    • 2018
  • Smart healthcare is convergence of ICT and healthcare services, and interdisciplinary research has been actively conducted in various fields. The objective of this study is to investigate trends of smart healthcare research using topic modeling and ego network analysis. Text analysis, frequency analysis, topic modeling, word cloud, and ego network analysis were conducted for the abstracts of 2,690 articles in Scopus from 2001 to April 2018. Topic Modeling analysis resulted in eight topics, Topics included "AI in healthcare", "Smart hospital", "Healthcare platform", "Blockchain in healthcare", "Smart health data", "Mobile healthcare", " Wellness care", "Cognitive healthcare". In order to examine the topic modeling results core deeply, we analyzed word cloud and ego network analysis for eight topics. This study aims to identify trends in smart healthcare research and suggest implications for establishing future research direction.

Travel analysis using Word Frequency (단어 빈도수에 의한 여행지역 특징 분석)

  • Park, JunSeong;Hwang, EunJi;Yun, JuYeon;Kang, TaeWon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.107-109
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    • 2015
  • 본 연구는 내일로 티켓을 이용하는 관광객을 대상으로 하는 연구다. 여행을 떠나는 관광객들을 분석하여 이들의 특징과 강원도, 전라도, 경상도를 방문하는 패턴을 파악하여 각 지역의 주요 여행지와 특징을 순위 표와 워드클라우드를 활용하여 효과적으로 정보를 제공하는 것을 목표로 한다.

Exploratory Big Data Analysis of Albert Camus's La Peste in Post Corona era (포스트 코로나 시대 알베르 카뮈의 『페스트』에 관한 탐색적 빅데이터 분석)

  • MIN, Jinyoung
    • The Journal of the Convergence on Culture Technology
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    • v.7 no.1
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    • pp.432-438
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    • 2021
  • This dissertation's object is to confirm the drastic popularity of La Peste of Albert Camus in Korea post-corona society using big data as the mean of inductive research. Analyzing news articles concerning Camus and investigating word frequency of the book La Peste will affirm the implications La Peste has on current Korea society as the outbreak spreads. As an analysis tool, Bigkinds of Korea Press Foundation and Nuagedemots, the French version of Word Cloud were used. For the past 30 years, Albert Camus has been known in Korea as the writer of L'étranger, but after the epidemic, he earned more reputation with La Peste. Compared to L'étranger that rebelled against the world's absurdity with ennui, La peste emphasizes the importance of resistance accompanied by solidarity. La peste conveys hope by depicting disastrous situations of citizens who confront the plague by organizing a health college. The novel delivers a lot of ethical inspiration to humanity in this exceptional circumstance of COVID-19.

Parents' Perceptions of Cognitive Rehabilitation for Children With Developmental Disabilities: A Mixed-Method Approach of Phenomenological Methodology and Word Cloud Analysis (발달장애 아동 부모의 인지재활 경험에 대한 질적 연구: 워드 클라우드 분석과 현상학적 연구 방법 혼합설계)

  • Ju, Yu-Mi;Kim, Young-Geun;Lee, Hee-Ryoung;Hong, Seung-Pyo;Han, Dae-Sung
    • Therapeutic Science for Rehabilitation
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    • v.13 no.1
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    • pp.49-63
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    • 2024
  • Objective : The purpose of this study was to investigate parental perspectives on cognitive rehabilitation using a combination of phenomenological research methodology and word cloud analysis. Methods : Interviews were conducted with five parents of children with developmental disabilities. Word cloud analysis was conducted using Python, and five researchers analyzed the meaning units and themes using phenomenological methods. Words with high frequency were considered as a heuristic tool. Results : A total of 43 meaning units and nine components related to the phenomenon of cognitive rehabilitation were derived, and three themes were finalized. The main themes encompassed the definition of cognitive rehabilitation, challenges associated with cognitive rehabilitation, and factors influencing the selection of a cognitive rehabilitation institute. Cognitive rehabilitation emerged as a treatment focused on improving learning, daily functioning, and cognitive abilities in children with developmental disabilities. The perceived issues with cognitive rehabilitation pertained to treatment methods, therapist expertise, and associated costs. In addition, parents highlighted the importance of therapist expertise, humane personality, and affordability of cost and schedule when choosing a cognitive rehabilitation institute. Conclusion : Parents expressed expectations for substantial improvements in their children's daily functioning through cognitive rehabilitation. However, challenges were identified in clinical practices. Going forward, we expect that cognitive rehabilitation will evolve into a better therapeutic support service addressing the concerns raised by parents.

N3WS : Interactive Newspaper Article Navigation Using Keyword and Summary Extraction (N3WS : 키워드 및 요약문장 추출을 이용한 인터랙티브 신문기사 탐색)

  • Cho, Hee-Jeong;Son, Ji-Youn;Yoon, Byeol-Yi;Cho, A-Hyun;Kim, Myung;Park, Eun-Jeong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.11a
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    • pp.694-697
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    • 2017
  • 최근 인터넷 기사 중에는 부정확한 제목이나 자극적인 단어를 사용하는 경우가 많아 구독자에게 불편함을 준다. 본 논문에서는 이러한 기사들의 헤드라인을 삭제하고, 기사의 내용을 3문장으로 요약해 주어, 구독자가 원하는 기사를 효율적으로 파악할 수 있게 하는 시스템을 제안한다. 제안하는 본 시스템은 파이썬 언어의 KoNLPy 패키지를 사용하여 기사의 단어들을 형태소 단위로 분석하며, 추출된 키워드를 토대로 워드 클라우드를 생성한다. 사용자가 클라우드의 특정 단어를 선택하면, 해당 신문기사들의 본문을 분석하여 각 신문 기사만의 핵심적인 문장을 3문장으로 출력해 준다.

Analysis of Domestic Research on Depression and Stress : Focused on the Treatment and Subjects (우울과 스트레스에 관한 국내 연구 분석 : 치료와 대상자를 중심으로)

  • Jo, Nam-Hee;Na, Eun-Young
    • Journal of Convergence for Information Technology
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    • v.7 no.6
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    • pp.53-59
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    • 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.

Necessity of the Physical Distribution Cooperation to Enhance Competitive Capabilities of Healthcare SCM -Bigdata Business Model's Viewpoint- (의료 SCM 경쟁역량 강화를 위한 물류공동화 도입 필요성 -빅데이터 비즈니스 모델 관점-)

  • Park, Kwang-O;Jung, Dae-Hyun;Kwon, Sang-Min
    • Management & Information Systems Review
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    • v.39 no.3
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    • pp.17-35
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    • 2020
  • The purpose of this study is to develop business models for current situational scenarios reflecting customer needs emphasize the need for implementing a logistics cooperation system by analyzing big data to strengthen SCM competitiveness capacities. For healthcare SCM competitiveness needed for the logistics cooperation usage intent, they were divided into product quality, price leadership, hand-over speed, and process flexibility for examination. The wordcloud results that analyzed major considerations to realize work efficiency between medical institutes, words like unexpected situations, information sharing, delivery, real-time, delivery, convenience, etc. were mentioned frequently. It can be analyzed as expressing the need to construct a system that can immediately respond to emergency situations on the weekends. Furthermore, in addition to pursuing communication and convenience, the importance of real-time information sharing that can share to the efficiency of inventory management were evident. Accordingly, it is judged that it is necessary to aim for a business model that can enhance visibility of the logistics pipeline in real-time using big data analysis on site. By analyzing the effects of the adaptability of a supply chain network for healthcare SCM competitiveness, it was revealed that obtaining competitive capacities is possible through the implementation of logistics cooperation. Stronger partnerships such as logistics cooperation will lead to SCM competitive capacities. It will be necessary to strengthen SCM competitiveness by searching for a strategic approach among companies in a direction that can promote mutual partnerships among companies using the joint logistics system of medical institutes. In particular, it will be necessary to search for ways to utilize HCSM through big data analysis according to the construction of a logistics cooperation system.

Analysis of Keyword Search Trends Related to Adolescents and Dietary Habits Before and After COVID-19 Using Text Mining (텍스트 마이닝을 이용한 코로나19 전후 청소년과 식생활 관련 키워드 검색 경향 분석)

  • Oh, Sang-Mi;Jung, Lan-Hee;Jeon, Eun-Raye
    • Journal of Korean Home Economics Education Association
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    • v.36 no.1
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    • pp.39-54
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    • 2024
  • This study analyzed Naver, Daum, Google, YouTube, and Twitter using TEXTOM for two years and four years as of January 18, 2020. The results are as follows. First, the total number and volume of keyword search data related to youth and diet were slightly higher after COVID-19, showing that interest increased due to COVID-19. Second, as a result of frequency analysis, 'education' was the highest before COVID-19, and 'health' was the highest after COVID-19, showing that interest in health is increasing due to the increased importance of health and immunity due to COVID-19. Third, as a result of frequency weight analysis of the top 50 keywords, 'education' showed the highest frequency before COVID-19, and 'acne' after COVID-19. Fourth, the results visualized using word cloud showed that the keywords 'education' before COVID-19 and 'health' after COVID-19 appeared the largest and boldest, showing the highest frequency and importance. As a result of the above results, we were able to use the text mining method to apply it to eating habits, and we used materials visualized as a highly readable word cloud in units such as eating problems in adolescence and balanced meal planning and selection in the home economics curriculum to improve the teaching of the class. The direction of proper eating habits education, including using it as a medium, was presented.

Content Analysis of Food and Nutrition Unit in High School Textbooks of Home Economics: Focus on the National Curriculums from 7th to 2015 Revised (고등학교 '기술·가정' 교과 식생활 영역의 교육내용 분석: 제7차 교육과정부터 2015 개정 교육과정까지의 교과서 내용을 중심으로)

  • Park, Chae Eun;Kim, Yoo Kyeong
    • Journal of Korean Home Economics Education Association
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    • v.31 no.4
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    • pp.97-113
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    • 2019
  • This study is focused on the examination of changes in textbooks of Home Economics in High school from 7st to 2015 curriculum, especially the 'Food and Nutrition section. We investigated the content elements of the National Curriculum Guide, the changes in learning contents, and the number of pages of Food and Nutrition section. The key words were extracted and the connective relationships between words were visualized using a method of language network analysis through word cloud and Semantic Network Analysis. According to the results of the research, the portion of the Food and Nutrition section has been gradually decreased on the Technology·Home Economics, following the development of the curriculum. Through the whole curriculum, 'invitation', 'Korean food', 'baby·nutrition' are appeared as key words. The education contents of Food and Nutrition section from the 7th to 2015 revised have been developed and advanced with the changes of social needs. However, the reduction of portion and insufficiency of content elements of Food and Nutrition section bring concerns toward the decline of the quality of education on dietary life.