• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2089-2094
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• 2017

Altmetrics is the measurement index and quantitative data to complement the traditional indicators based on the citation. Altmetrics research has acquired greater importance in the past few years, partly due to the complement to the traditional bibliometrics. This paper aims to reveal the research status and trends in altmetrics research. A total of 187 articles from 2005 to 2017 are obtained and analyzed, illustrating a steady rise (S-mode) in altmetrics research since 2005. Using social network analysis and co-word analysis, the author cooperation network and keyword co-occurrence network are developed. The core scientists and eight international research groups are discovered, reflecting that researchers in this field have a low degree of cooperation. Four topics of altmetrics research are discovered by hierarchical clustering. The results can be useful for the advanced research of altmetrics.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.77-78
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• 2018

알트메트릭스 분석 개념이 국내에 도입된지 6-7년이 되었지만, 아직 대중적인 확산이 아쉬운 상황이다. 특히 소셜네트워크가 아닌 국내 학술정보에 대한 알트메트릭스 분석을 위한 콘텐츠 및 분석기반이 활성화되지 못하고 있다. 본 연구에서는 학술정보에 대한 알트메트릭스 연구동향을 살펴보고, 국내 학술지에 대한 활성화 방안을 제시하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.117-120
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• 2011

최근의 디지털 기술 및 컴퓨터, 통신 기술의 개발과 확산을 통해 다양한 소셜 미디어의 사용에 따른 디지털 사회가 생성되고 디지털 지능이 출현하여 새로운 디지털 문화와 커뮤니티가 생겨나고 있다. 이러한 추세에 따라 산업체, 교육계 등에서 소셜 미디어의 사용 및 채택 효과에 대한 연구가 필요하다. 또한, 소셜 도구의 사용 기법과 활용 방안이 필요하며, 사용 실태에 대한 조사가 필요하다. 이에 따라, 이 논문에서는 소셜 미디어를 개인 및 회사의 업무에 사용할 수 있는 있는 활용 방안을 마련하기 위하여, 소셜 미디어의 참여와 접근 방안 등에 대한 방법론을 조사하고 제시한다. 소셜 미디어에 접근하고 활용하기 위하여 웹 로그 분석 도구 및 소셜 미디어 콘텐츠 도구 등에 대하여 살펴보고, 웹 사이트 분석과 활용 방안 마련을 위하여 소셜 미디어 메트릭스 활용 방안을 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.182-183
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• 2018

Big data analysis is the process of discovering a meaningful correlation, pattern, and trends in large data set stored in existing data warehouse management tools and creating new values. In addition, by extracts new value from structured and unstructured data set in big volume means a technology to analyze the results. Most of the methods of Big data analysis technology are data mining, machine learning, natural language processing, pattern recognition, etc. used in existing statistical computer science. Global research institutes have identified Big data as the most notable new technology since 2011.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.367-376
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• 2020

According to recent statistics on new consumer market trends, 'alone consumption' is at the center. This study focuses on the social big data that attracts the public's opinions in that it is important for a certain social trend to comprehensively understand the various fields such as society, locality, culture, marketing, economics, and psychology that form the background for it. Therefore, we set up the linkage of 'solo consumption' and conducted research on new consumer market trends using Opinion Analisys. As a result of this trend analysis, representative keywords such as 'honbab', 'honsul' and 'honyoeng' were derived and analyzed the trend of new consumer market using this data. Alone consumption is an inevitable new consumption trend caused by demographic change after the global economic crisis. The importance as a trend reflecting this will be further strengthened. Trend analysis by social big data will help scientific and systematic business distribution strategies and planning to help make new and valuable decisions and decisions about new consumer markets.

• Journal of Communication Design
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• v.63
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• pp.336-347
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• 2018

Nowadays, in Korea, the number of campers is increased over 5 million. Many Korean camping brands have excellent qualities however, a lot of times weak brand identities to be globally known. The purpose of this study is to provide helpful sources to have strong brand identities, add more values based on related word research from big data and market research. The data is to be analysed by sensible & cognitive approaches. The keywords for the sensible research are 'camping, camp, camping brand, and camping design'. Then 17 representative oversea brands and 10 Korean brands were analysed for the market researches. From related word research from big data, we can find out the thinking process of potential consumers, how people communicates to exchange information, and what can be the sources to add brand values. Also from the market researches, we were able to find that successful brands have distinctive brand identities, stories, logos with representable colors and they continuous produce signature designs and own way of color matching.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.164-172
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• 2018

This paper examines the relevance of pre-production and storytelling in big data analysis and, focusing on JTBC's Man to Man series, looks at how the drama's storytelling should be structured. In this study, we conducted text mining on blogs focused on a particular topic to read the viewer's thoughts on pre-produced dramas and on 67 blogs written about Pre-Production Dramas from 2016.12.15 to 2017.12.15. Also, we conducted sentiment analysis about the Man to Man series, which is not only a pre-production drama, but also has storytelling issues. The blog text extraction and text mining were analyzed using the OutWit Hub and the R, and the tools.provided by social metrics were used to make sentiment analyses of the larger data. Sentiment analysis revealed that the viewers of the Man to Man series did not agree with the romance between Kim Sul-woo and Cha Do-ha, due to the lack of reality in the female characters. Therefore, it was concluded that it is crucial to increase the reality of the characters in order to increase the audience's empathy. These studies will continue to be necessary, because they will form the basis for digitally driven storytelling studies and will provide valuable materials for conducting predictions and instructions in the cultural content industry.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.739-746
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• 2020

The fourth industrial revolution introduced at world economic forum in 2016 has had huge effects on tourism industries as well as the change of core technologies in ICT such as big data, IoT, etc, This paper proposes the methods to propel tourism of Yoesu city through big data analysis and questionnaires. Sensitive words and positive-negative trend are extracted by Social Metrics and the keywords for Yeosu tour trends are extracted and analyzed by Naver datalab, and the results are visualized by R language. And frequency, difference, factor, covariance and regression analysis in SPSS are executed for the questionnaires for 493 visitors who traveled in Yeosu city. Sentiment analysis for Yeosu tour and maritime cable car shows that positive effect is much more than negative one. The analyses for questionnaires in SPSS show that Yeosu area is statistically significant to tour satisfaction index and tour revitalization for Yeosu, and favorite sightseeing places and searching electronic devices for age groups are different. The sightseeing places such as a maritime park with soft contents that give joyfulness and healing to tourists are highly attracted in both the big data and questionnaires analysis.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.