• 한국콘텐츠학회논문지
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• 제18권6호
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• pp.217-234
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• 2018

본 연구는 국내외 스포츠용품 브랜드에 대한 빅데이터 분석을 실시하였다. 이를 위해 소셜 매트릭스 프로그램인 텍스톰과 패션데이터 분석 플랫폼인 MISP를 통해 텍스트 마이닝, TF-IDF, 오피니언 마이닝, 관심도 그래프를 실시하였으며, 스포츠브랜드에 대한 최근 인식을 살펴보기 위해 2017년 1월 1일부터 2017년 12월 31일까지 1년간을 연구대상 기간으로 한정하였다. 분석 결과, 첫째, 각 브랜드를 대표하는 상품을 확인할 수 있었다. 둘째, 각 브랜드를 대표하는 마케팅을 확인할 수 있었다. 셋째, 각 브랜드에서 공통적으로 추출된 단어를 확인할 수 있었다. 넷째, 각 브랜드의 긍정 및 부정에 대한 감정을 확인할 수 있었다.

• International Journal of Contents
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• 제15권1호
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• pp.10-24
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• 2019

This study applied network agenda-setting theory to analyze the impact of the agenda-setting function of the media on certain issues by focusing on the agenda at the center of controversy, 'Creative Economy'. To this end, the study extracted the data referred to creative economy in the media and SNS from 1 January 2008 to 31 December 2014, and analyzed the data using the network analysis program UCINET and the Korean language analysis program Textom. The results of the present study show that, during the period under former President Lee (2008-2011), the media's creative economy agenda-setting function did not exert a significant impact on the agenda-setting within SNS. However, from 2012 when the government of former President Park Geun-hye had started, the agenda-setting function of the media starts to show increasingly strong influence on the agenda cognition in SNS. The central words and sub-words configuration forming the center of the semantic network moved in the direction of a high correlation, in addition to the gradually increasing correlation based on QAP correlation analysis. In 2014, the semantic networks of the media and SNS bore a close resemblance to each other, while the shape of networks and sub-words structure also had a high level of similarity.

• 한국병원경영학회지
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• 제23권4호
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• pp.26-39
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• 2018

Purposes: The purpose of this study is to analyze the issue of interest in patient medical service of small and medium hospitals using big data. Methods: The method of this study was implemented by data mining and social network using SNS big data. The analysis tool were extracted key keywords and analyzed correlation by using Textom, Ucinet6 and NetDraw program. Findings: In the results of frequency, the network-centered and closeness centrality analysis, It was shown that the government center is interested in the major explanations and evaluations of the technology, information, security, safety, cost and problems of small and medium hospitals, coping with infections, and actual involvement in bank settlement. And, were extracted care for disabilities such as pediatrics, dentistry, obstetrics and gynecology, dementia, nursing, the elderly, and rehabilitation. Practical Implications: Future studies will be more useful if analyzed the needs of customers for medical services in the metropolitan area and provinces may be different in the small and medium hospitals to be studied, further classification studies.

• International Journal of Internet, Broadcasting and Communication
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• 제13권4호
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• pp.72-78
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• 2021

Currently, the Korean wave is not limited to popular culture, but has a significant impact not only on Korea's national image but also on the improvement of Korean companies' products and image of Korea. In this study, using Textom to confirm the change in foreign tourists' image of Korea, the data collection period was 1 year of 2020, when COVID 19 occurred, as a collection period for "Korea and foreigner" and related key words, each Hallyu content, and ranked in the top 80 keywords were derived. Centrality analysis for semantic network visualization was performed using UCINET6, and through CONCOR analysis, 7 groups 'K-Quarantine ', 'K-Drama', 'K-Movie', 'K-Beauty', 'K-Shopping', It was clustered into 'K-Tech' and 'K-Pop'. As a result of the analysis, the image of Korea abroad generally recognized the Korean Wave as cultural content, but after the outbreak of COVID 19, it is judged that it has been recognized as a country with a successful case of K-Quarantine.

• 한국의류학회지
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• 제44권5호
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• pp.1019-1035
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• 2020

This study examines the millennial generation, who express themselves and share information on social media after experiencing constantly changing 'hot places' (places of interest) in contemporary cities, with the goal of analyzing space consumption behaviors. Data were collected via an Instagram crawler application developed with Python 3.4 administered to 19,262 posts using the term 'hot places' from November 1 and December 15, 2019. Issues were derived from a text mining technique using Textom 2.0; in addition, semantic network analysis using Ucinet6 and the NetDraw program were also conducted. The results are as follows. First, a frequency analysis of keywords for hot places indicated words frequently found in nouns were related to food, local names, SNS and timing. Words related to positive emotions felt in experience, and words related to behavior in hot places appeared in predicate. Based on importance, communication is the most important keyword and influenced all issues. Second, the results of visualization of semantic network analysis revealed four categories in the scope of the definition of "hot place": (1) culinary exploration, (2) atmosphere of cafés, (3) happy daily life of 'me' expressed in images, (4) emotional photos.

• 지역사회간호학회지
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• 제32권4호
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• pp.467-476
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• 2021

Purpose: The purpose of this study is to understand the social perceptions of nurses in the context of the COVID-19 outbreak through analysis of media articles. Methods: Among the media articles reported from January 1st to September 30th, 2020, those containing the keywords '[corona or Wuhan pneumonia or covid] and [nurse or nursing]' are extracted. After the selection process, the text mining and topic modeling are performed on 454 media articles using textom version 4.5. Results: Frequency Top 30 keywords include 'Nurse', 'Corona', 'Isolation', 'Support', 'Shortage', 'Protective Clothing', and so on. Keywords that ranked high in Term Frequency-Inverse Document Frequency (TF-IDF) values are 'Daegu', 'President', 'Gwangju', 'manpower', and so on. As a result of the topic analysis, 10 topics are derived, such as 'Local infection', 'Dispatch of personnel', 'Message for thanks', and 'Delivery of one's heart'. Conclusion: Nurses are both the contributors and victims of COVID-19 prevention. The government and the nurses' community should make efforts to improve poor working conditions and manpower shortages.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.41-46
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• 2022

The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.

• Human Ecology Research
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• 제59권2호
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• pp.245-259
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• 2021

The purpose of this study was to analyze big data to identify the sub-dimensions of ethical consumption, as well as the consumption value associated with ethical consumption that changes over time. For this study, data were collected from Naver and Daum using the keyword 'ethical consumption' and frequency and matrix data were extracted through Textom, for the period January 1, 2016, to December 31, 2018. In addition, a two-way mode network analysis was conducted using the UCINET 6.0 program and visualized using the NetDraw function. The results of text mining show increasing keyword frequency year-on-year, indicating that interest in ethical consumption has grown. The sub-dimensions derived for 2014 and 2015 are fair trade, ethical consumption, eco-friendly products, and cooperatives and for 2016 are fair trade, ethical consumption, eco-friendly products and animal welfare. The results of deriving consumption value keywords were classified as emotional value, social value, functional value and conditional value. The influence of functional value was found to be growing over time. Through network analysis, the relationship between the sub-dimensions of ethical consumption and consumption values derived each year from 2014 to 2018 showed a significantly strong correlation between eco-friendly product consumption and emotional value, social value, functional value and conditional value.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.204-210
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• 2022

From the 1970s to the present, the number of new infectious diseases such as SARS, Ebola virus, and MERS has steadily increased. The new infectious disease, COVID-19, which began in Wuhan, Hubei Province, China, has pushed the world into a pandemic era. As a result, Countries imposed restrictions on entry to foreign countries due to concerns over the spread of COVID-19, which led to a decrease in the movement of tourists. Due to the restriction of travel, keywords such as "Corona blue" have soared and depression has increased. Therefore, this study aims to analyze the stress meaning network of the COVID-19 era to derive keywords and come up with a plan for a travel-related platform of the Post-COVID 19 era. This study conducted analysis of travel and stress caused by COVID-19 using TEXTOM, a big data analysis tool, and conducted semantic network analysis using UCINET6. We also conducted a CONCOR analysis to classify keywords for clustering of words with similarities. However, since we have collected travel and stress-oriented data from the start to the present, we need to increase the number of analysis data and analyze more data in the future.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.222-230
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• 2023

The purpose of this study is to provide basic data on sports entertainment programs by collecting data on unstructured data generated by Naver and Google for SBS entertainment program 'Women Who Score Goal', which began regular broadcast in June 2021, and analyzing public perceptions through data mining, semantic matrix, and CONCOR analysis. Data collection was conducted using Textom, and 27,911 cases of data accumulated for 16 months from June 16, 2021 to October 15, 2022. For the collected data, 80 key keywords related to 'Kick a Goal' were derived through simple frequency and TF-IDF analysis through data mining. Semantic network analysis was conducted to analyze the relationship between the top 80 keywords analyzed through this process. The centrality was derived through the UCINET 6.0 program using NetDraw of UCINET 6.0, understanding the characteristics of the network, and visualizing the connection relationship between keywords to express it clearly. CONCOR analysis was conducted to derive a cluster of words with similar characteristics based on the semantic network. As a result of the analysis, it was analyzed as a 'program' cluster related to the broadcast content of 'Kick a Goal' and a 'Soccer' cluster, a sports event of 'Kick a Goal'. In addition to the scenes about the game of the cast, it was analyzed as an 'Everyday Life' cluster about training and daily life, and a cluster about 'Broadcast Manipulation' that disappointed viewers with manipulation of the game content.