• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Health Policy and Management
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• v.29 no.2
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• pp.138-145
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• 2019

Background: The purpose of this study is supposed to figure out the keyword network from 2009 to 2018 with social network analysis and provide the research data that can help the Korea government's policy making on smoking cessation. Methods: First, frequency analysis on the keyword was performed. After, in this study, I applied three classic centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) with R 3.5.1. Moreover, I visualized the results as the word cloud and keyword network. Results: As a result of network analysis, 'smoking' and 'smoking cessation' were key words with high frequency, high degree centrality, and betweenness centrality. As a result of looking at trends in keyword, many study had been done on the keyword 'secondhand smoke' and 'adolescent' from 2009 to 2013, and 'cigarette graphic warning' and 'electronic cigarette' from 2014 to 2018. Conclusion: This study contributes to understand trends on smoking cessation study and seek further study with the keyword network analysis.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.279-285
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• 2023

This study provides academic implications by considering trends of domestic research regarding therapy for Mental disorder schizophrenia and psychosocial. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 65 papers have been collected The result of this study is as follows. First, collected data were visualized through analysis of keywords by using word cloud method. Second, keywords such as intervention, schizophrenia, research, patients, program, effect, society, mind, ability, function were recorded with highest frequency resulted from keyword frequency analysis. Third, LDA (latent Dirichlet allocation) topic modeling result showed that classified into 3 keywords: patient, subjects, intervention of psychosocial, efficacy of interventions. Fourth, the social network analysis results derived connectivity, closeness centrality, betweennes centrality. In conclusion, this study presents significant results as it provided basic rehabilitation data for schizophrenia and psychosocial therapy through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of schizophrenia and psychosocial therapy through text mining and social network analysis.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.99-114
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• 2019

In this study we examined the preferences of eight college living factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the analysis results of text mining were visualized as word cloud. The college life factors included eight topics that were closely related to students: 'my present', 'my 10 years later', 'friendship', 'college festival', 'student restaurant', 'college dormitory', 'KNCAF', and 'long-term field practice'. In the text submitted by the students, we have established a dictionary of positive words and negative words to evaluate the preference by classifying the emotions of positive and negative. As a result, KNCAF students showed more than 85% positive emotions about the theme of 'student restaurant' and 'friendship'. But students' positive feelings about 'long-term field practice' and 'college dormitory' showed the lowest satisfaction rate of not exceeding 60%. The rest of the topics showed satisfaction of 69.3~74.2%. The gender differences showed that the positive emotions of male students were high in the topics of 'my present', 'my 10 years later', 'friendship', 'college dormitory' and 'long-term field practice'. And those of female were high in 'college festival', 'student restaurant' and 'KNCAF'. In addition, using text mining technique, the main words of positive and negative words were extracted, and word cloud was created to visualize the results.

• Journal of Sport and Applied Science
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• v.7 no.4
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• pp.1-5
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• 2023

Purpose: The purpose of the study is to analyze the perception of swimming rating system. Research design, data, and methodology: In this study, practitioners and leaders of Korean swimming federation were selected as the subjects of the study to institutionalize the grade of swimming. Data analysis was categorized according to word frequency after coding using the Nvivo 12.0 program, and words were visualized using the word cloud program. PASW/WIN 21.0 was employed to analyze demographic characteristics. Triangular verification and expert meetings were conducted three times to increase the validity of the study. In these meetings, the study excluded subjective interpretation and errors of the researcher. Results: First, as a result of analyzing the perception of practitioners before educational training, 16 words and the total frequency of words was 21 times. Second, as a result of analyzing the perception of practitioners after educational training, 22 words and a total of 25 frequencies were found. Third, as a result of analyzing the leader's perception before educational training, 32 words and the total frequency of words was 63 times. Fourth, as a result of analyzing the leader's perception after educational training, 41 words and a total of 72 frequencies were found. Conclusions: Findings indicated divers feelings and thoughts of practitioners and leaders of Korean swimming federations towards swimming rating system. Further implications were discussed.

• Knowledge Management Research
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• v.21 no.3
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• pp.215-228
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• 2020

This study introduces the case of a company that newly jumped into the competitive cosmetics market with a brand concept developed through big data analysis. Skin Reverse Lab, which possesses anti-aging material technology, launched a new brand in the skincare cosmetics market. Using a big data analysis program called Luminoso, SNS data was analyzed in four areas, which were consumer attitudes toward overall cosmetics, skincare products, competitors, and consumers' experiences of product use. The age groups and competitors were analyzed through the emotional analysis technique including context, which is the strength of Luminoso, and insights on consumers were derived through the related word analysis and word cloud techniques. Based on the analysis results, Logically Skin have won various awards in famous magazines and apps, and have been recognized as products that meet global trend standards. Besides, it has entered six countries including the United States and Hong Kong. The Logically Skin case is a case in which a new company entered the market with a new brand by deriving consumer insights only from external data, and it is significant as a case of applying AI-based sentiment analysis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.175-177
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• 2024

OTT(Over-The-Top) 플랫폼의 부상은 미디어 콘텐츠 소비 방식을 혁명적으로 변화시키고 있다. 본 논문은 Netflix, Amazon Prime Video, Disney+, Hulu 등 주요 OTT 플랫폼에 등록된 영화들을 IMDb 평점과 러닝타임, Rotten Tomatoes 지수를 중심으로 분석한다. 이를 통해 현재의 영화 시장 트렌드와 소비자 선택, 시장 전략에 중요한 정보를 제공하려 한다. 분석 결과, 플랫폼별로 제공하는 영화의 품질과 러닝타임이 다양하며, 소비자들이 선호하는 영화 테마를 시각적으로 파악할 수 있는 워드 클라우드를 포함한다. 이러한 결과는 OTT 플랫폼의 전략적 콘텐츠 제공과 소비자 행동 이해에 기여할 수 있는 중요한 통찰력을 제공한다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.189-200
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• 2021

Recently, Information and Communication Technology and SMART Phone Technology have been rapidly developed. According to the increase of data use, the era of big data has come. With the approach of non-contact society, QR Codes are becoming inseparable in our lives. In this paper, we are trying to figure out the implications of QR Code research based on Big Data in Korea. The purpose of this study is to first examine the previous studies on "QR Code" and conduct an analysis on keywords by field using Big Data. Second, for data visualization WordCloud analysis and network analysis are performed on "QR Code" frequent keyword. Third, we would like to present the research direction to future researchers regarding "QR Code". In the results, First of all, research trends showed that research is on the rise and that various fields are being utilized. Second, the results of the analysis of frequent keyword resulted in similar results overall, with some differences depending on the field and year. Third, we found that the visualization results according to the frequent keyword were also analyzed in the same way as the frequent keyword analysis results. The practical implications of the theoretical findings are as follows. First, 'QR Code' needs to be studied as a means of information delivery, not as a technical aspect. Second, it can be seen that "QR Code" is developing reflecting social trends or issues. With both theoretical and practical implications, we are trying to provide the strategic ways of QR-code in future.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.460-469
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• 2020

This study analyzes online news articles and cafe articles on the '5-day Rotation Mask Distribution System', which is emerging as a recent issue due to the COVID-19 incident, to identify the mass media and social media agendas containing media and public reactions. This study figured out the difference between mass media and social media. For analysis, we collected 2,096 full text articles from Naver and 1,840 posts from Naver Cafe, and conducted word frequency analysis, word cloud, and LDA topic modeling analysis through data preprocessing and refinement. As a result of analysis, social media showed real-life topics such as 'family members' purchase', 'the postponement of school opening', ' mask usage', and 'mask purchase', reflecting the characteristics of personal media. Social media was found to play a role of exchanging personal opinions, emotions, and information rather than delivering information. With the application of the research method applied to this study, social issues can be publicized through various media analysis and used as a reference in the process of establishing a policy agenda that evolves into a government agenda.

• Fashion & Textile Research Journal
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• v.24 no.5
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• pp.593-604
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• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.