• Journal of Digital Convergence
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• v.17 no.9
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• pp.47-54
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• 2019

This study confirms the polarity of news articles on apartment prices using Opinion Mining which has widely been used for a big data analysis. The analyses were carried out utilizing internet news articles posted on the Naver for two years: 2012 and 2018. We proposed a sentiment analysis model and modeled a topic-oriented sentiment dictionary construction methods. As a result of analyzing the proposed sentiment analysis model, it was confirmed that there was a difference according to the tendency of the media companies in selecting social issues at the time of rising apartment prices. At the same time, we were able to find more affirmative articles in the media companies which share similar sentiment with the government in charge. In this paper, we proposed a sentiment analysis model that can be used in real estate field and analyzed the polarity of unformatted data related to real estate. In order to integrate them into various fields in the future, it is necessary to build the sentiment dictionaries by themes, as well as to collect various unformatted data over extended periods.

• Journal of Information Technology Services
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• v.20 no.2
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• pp.77-90
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• 2021

Online and mobile games represent digital entertainment. Not only the game grows fast, but also it has been noted for unique business models such as a subscription revenue model and free-to-play with partial payment. But, a recent revenue mechanism, called a loot-box system, has been criticized due to overspending, weak protection to teenagers, and more over gambling-like features. Policy makers and research communities have counted on expert opinions, review boards, and temporal survey studies to build countermeasures to minimize negative effects of online and mobile games. In this process, speed was not seriously considered. In this study, we attempt to use a big data source to find a way of observing a trend for policy makers and researchers. Specifically, we tried to apply the Word2Vec data mining algorithm to news repositories. From the findings, we acknowledged that the suggested design would be effective in lightening issues timely and precisely. This study contributes to digital entertainment service communities by providing a practical method to follow up trends; thus, helping practitioners have concrete grounds for balancing public concerns and business purposes.

• Convergence Security Journal
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• v.17 no.5
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• pp.111-119
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• 2017

As of March 2017, fake news is largely focused on political issues. Outside the country, main issues of the fake news have been a hot topic in the US presidential election in 2016 and emerged as a new political and social problem in countries like Germany and France. In Korea, issues of the fake news are also linked with political issues such as presidential impeachment and prosecution, impeachment quota, early election, etc. This phenomenon has recently led to the production and spread of fake news related to safety and security issues as well as political issues in connection with various methods of generating articles and sharing information. As a result, there is a high possibility that the information will be transformed into information that can cause considerable confusion to the public. Therefore, the recognition of such problems means that it is important at this point to consider the related situation analysis and effective countermeasures. To do this, we tried to make accurate and meaningful analysis for the diagnosis, analysis, forecasting and management of issues utilizing Big Data. As a result, it is found that the fake news is continuously generated in relation to the safety and security issue as well as the political issue in the South Korea, and differs from the general form occurring outside the country.

• Journal of Broadcast Engineering
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• v.22 no.4
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• pp.438-447
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• 2017

The purpose of this study is to understand the recognition of gambling industry through the semantic analysis of news data on lottery, sports betting, horse racing and casino that was reported between 1990 to 2015 in South Korea. This paper revealed the difference between journalists' intention and public's perception about news by analyzing the frequency and connectivity of news with framing and public's interest through semantic network analysis and explored the policy characteristics and innovation task. The result of analysis, news on lottery game mainly has been reported social issue related with win such as 'winning number', 'prize money', 'suspicion of manipulation' and etc. News on sports betting has been reported mandatory information related with business project and illegal site such as 'bidding', 'illegal site', 'sales target' and etc. News about horse racing has been reported the information about the business advertisement such as 'online race track' and 'promotion'. Lastly, casino related news has been reported 'major information' such as illegality', 'gambling place' and 'foreigner'. As a result of times series analysis, news about casino in the 1990s, news about lottery in the 2000s and news about horse racing in 2010s have been increased. Public's interest also has been moved to 'business scandal', 'winning game', 'citizens' campaign' and etc. Gambling related news has been classified by four types, 1. advertising publicity(horse racing), 2. mandatory information(sports betting), 3. social issue(public agenda, lottery), 4. major information(casino). We could get the insight that news can be formed a public agenda, when news is reported as a social issue with high frequency and public's interest like lottery related news.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.167-176
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• 2018

In this study, Big Data was used to collect the information related to 'Cooking Academy' keywords. After collecting all the data, we calculated the frequency through the text mining and selected the main words for future data analysis. Data collection was conducted from Google Web and News during the period from January 1, 2013 to December 31, 2017. The selected 64 words were analyzed by using UCINET 6.0 program, and the analysis results were visualized with NetDraw in order to present the relationship of main words. As a result, it was found that the most important goal for the students from cooking school is to work as a cook, likewise to have practical classes. In addition, we obtained the result that SNS marketing system that the social sites, such as Facebook, Twitter, and Instagram are actively utilized as a marketing strategy of the institute. Therefore, the results can be helpful in searching for the method of utilizing big data and can bring brand-new ideas for the follow-up studies. In practical terms, it will be remarkable material about the future marketing directions and various programs that are improved by the detailed curriculums through semantic network of cooking school by using big data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.183-184
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• 2022

본 연구는 장애인 평생교육 관련 언론사 뉴스 빅데이터를 한국언론재단의 빅카인즈(BIGKinds) 시스템을 이용하여 분석하였다. 본 연구에서는 2000년 1월 1일부터 2020년 12월 31일까지 20년간, 총 54개 언론사에서 보도한 '장애인 평생교육' 관련 뉴스 기사들을 추출하였다. 그 분석대상 뉴스 빅데이터를 대상으로 키워드 트렌드 분석, 언어 네트워크 지도 구현, 연관어 분석(워드클라우드 제시) 등을 진행하였다. 본 연구 결과는 장애인 평생교육 관련 정책 입안 연구 및 실증적인 연구(평생교육 참여 요인 및 효과 등)의 기초자료로 활용될 수 있을 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.351-354
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• 2022

본 연구는 최근 2022년 6월 1일에 실시된 전국 시도교육청 교육감 선거를 계기로 진행된 연구이다. 본 연구의 목적은 2010년 1월 1일부터 2022년 6월 10일까지 '교육감 직선제'를 다룬 언론사 기사들을 분석하여 그 결과를 객관적으로 제시하는 것이다. 분석 대상은 2010년 1월 1일부터 2022년 6월 10일까지 기간을 설정한 후, '교육감'과 '직선제' 2개의 용어가 모두 포함된 국내 54개 주요 언론사 뉴스 기사들(5,610건)이다. 본 연구에서는 뉴스 빅데이터 분석시스템인 빅카인즈(BIGKinds) 서비스를 적극적으로 이용하여 뉴스 트렌드 분석, 네트워크(관계도) 분석, 연관어 분석 등을 진행하였다. 본 연구자료는 관련 학문 연구자와 교육 현장 종사자들에게 시사점을 줄 수 객관적인 자료로 활용될 것이다. 본 연구는 향후 지방교육자치와 교육감 선거의 발전적 모델 탐색을 위한 다양한 연구 과정으로 확대 전개하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.448-450
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• 2016

정보기술과 디지털 경제의 확산으로 대규모의 데이터가 생산되는 정보화시대에서 빅 데이터의 중요성이 강조되고 있으며 다양한 분야에서 이를 응용하고 있다. 빅 데이터 분석도구인 R은 통계 기반의 정보 분석을 가능하게 하는 언어와 환경이다. 본 논문에서는 R을 이용하여 기상뉴스에 나타난 기상관련 빅 데이터를 분석한다. 다양한 뉴스에서 기상 관련 데이터를 수집하고 어떠한 텍스트가 분포되어 있는지 빈도 조사를 수행한다.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.173-183
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• 2019

This study aims to utilize big data contents of news and social media for developing a corporate strategy of multinational enterprises and their global decision-making through the data mining technique, especially text mining. In this paper, the data of 2 news media (BBC and CNN) and 2 social media (Facebook and Twitter) were collected for the three global leading Korean companies (Samsung, Hyundai Motor Company, and LG) from April, 2018 to April, 2019. The findings of this paper have shown that traditional news media and also modern social media have become devastating tools to extract global trends or phenomena for businesses. Moreover, this presents that a company can adopt a two-track strategy through two different types of media by deriving the key issues or trends from news media channels and also grasping consumers' sentiments, preference or issues of interest such as battery or design from social media. In addition, analyzing the texts of those media and understanding the association rules greatly contribute to the comparison between two different types of media channels to see the difference. Lastly, this provides meaningful and valuable data empowered insights to find a future direction comprehensively and develop a global strategy for sustainability of business.

• Journal of Korean Academy of Nursing
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• v.47 no.6
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• pp.806-816
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• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.