• Fashion & Textile Research Journal
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• v.21 no.1
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• pp.27-35
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• 2019

Fashion Influencer of Instagram get a lot of attention from the public, and they play a major role in shaping peoples' taste. This study attempts to analyze the fashion design of fashion influencer in Instagram. The data was collected from Apr. 15th to April 30th, 2017, and the pictures were collected from May, 2016 to April, 2017. Total of 460 pictures were collected based on the number of "likes". The method of study was content analysis and the cross tabulation analysis and frequency using SPSS Statics 24 Based on the above results, influencers were mostly models that have many "likes" on their photos. Many of influencers were wearing black, white, or blue dresses that do not have any patterns. Many others were wearing indigo, black, or white jeans with T-shirts. In summary of the above contents, influencer also found out that the materials of their clothes were both hard and soft, and that the casual style was the most popular among influencer, and that influencer also liked elegant, modern, mannish, or sexy looks. Therefore, through this study, it was found that the fashion design of influencer had a unique fashion image. Gigi Hadid, Kendall Jenner, and Blake Lively are the representative influencers of fashion instagram. Gigi Hadid was a casual and manish image, Kendall Jenner was a casual and sexy image, and Blake Lively was an elegant image.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.115-123
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• 2021

This study aims to analyze the changes in public perception of abandoned dogs through big data analysis. Data from January 2017 to July 2020 were collected to analyze how the quantitative change in social issues with abandoned dogs as a keyword had an effect on public perception of abandoned dogs, and factors that influence positive/negative perceptions. As a result of the study, it was confirmed that the number of stray dogs and the number of documents related to stray dogs had a positive correlation, and specific time series changes were found through various analysis techniques such as text mining, network analysis, and sentiment analysis. This study will have significance as basic data that can be used for policy establishment or other research on abandoned dogs. we hope it will help to solve problems so as to improve awareness of abandoned dogs and develop a sense of responsibility.

• Smart Media Journal
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• v.6 no.1
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• pp.47-53
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• 2017

Social issues are important factors that decide government policy and newspapers are critical channels that reflect them. Analysing news articles can contribute to understanding social issues, but it is very difficult to analyse the unstructured large volumes of news data manually. Therefore, this study aims to analyze the different views among stakeholders of a specific social issue by using text analysis, word cloud analysis and associative analysis methods, which systematically transform unstructured news data into structured one. We analyzed a total of 115 news articles and a total of 6,772 comments, collected from the selected newspapers (Chosun-Il-bo, Joongang-Il-bo, Donga-Il-bo, Maeil Newspaper, Busan-Il-bo) for two weeks. We found that there are significant differences in tone between newspapers. While nation-wide daily newspapers focus on political relations with local areas, local daily newspapers tend to write articles to represent local governments' interests.

• 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.

• Industry Promotion Research
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• v.8 no.4
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• pp.131-137
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• 2023

This study collected and analyzed articles related to military sex crimes covered in the news from February 2019 to May 28, 2022 in order to identify problems arising from sexual crimes in the military. In order to understand the current status of military sexual violence reported in the media, articles were collected using BIGKinds, a news big data analysis system, and using the Textom program, the study was conducted using frequency analysis by period, word cloud, and semantic network analysis techniques for keywords. The study was conducted using the technique. As a result of data analysis, first, it was confirmed that the public's attention was focused on the victims in reports related to sex crimes within the military. Second, the problem of the lukewarm system of the relevant authorities in responding to sex crimes was revealed. Third, there was a lack of support for victims of sex crimes.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.115-126
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• 2021

Central banks communicate with the market through a statement on the direction of monetary policy while implementing monetary policy. The rapid contraction of the global economy due to the recent Covid-19 pandemic could be compared to the crisis situation during the 2008 global financial crisis. In this paper, we analyzed the text data from the monetary policy statements of the Bank of Korea and Fed reflecting monetary policy directions focusing on how they were affected in the face of a global crisis. For analysis, we collected the text data of the two countries' monetary policy direction reports published from October 1999 to September 2020. We examined the semantic features using word cloud and word embedding, and analyzed the trend of the similarity between two countries' documents through a piecewise regression tree model. The visualization result shows that both the Bank of Korea and the US Fed have published the statements with refined words of clear meaning for transparent and effective communication with the market. The analysis of the dissimilarity trend of documents in both countries also shows that there exists a sense of synchronization between them as the rapid changes in the global economic environment affect monetary policy.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.67-82
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• 2019

The purpose of this study is to analyze the issues and perceptions of smart cities. First, based on the big data analysis platform, big data analysis on smart cities were conducted to derive keywords by year, word cloud, and frequency of generation of smart city keywords by time. Second, trend and flow by area were analyzed by reclassifying major keywords by year based on meta-keywords. Third, emotional recognition flow for smart cities and major emotional keywords were derived. While U-City in the past is mostly centered on creating infrastructure for new towns, recent smart cities are focusing on sustainable urban construction led by citizens, according to the analysis. In addition, it was analyzed that while infrastructure, service, and technology were emphasized in the past, management and methodology were emphasized recently, and positive perception of smart cities was growing. The study could be used as basic data for the past, present and future of smart cities in Korea at a time when smart city services are being built across the country.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.1
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• pp.447-467
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• 2018

Biographical information about people is usually collected and provided by a company or an institute which has a specific standard to select people for service. Recently, user oriented contents service like Wikipedia has started biographical information service, Wikipedia Biography Portal, in which users select people and freely describe about them. This study collected 500 biographical data from three categories of Wikipedia biography portal such as criminals, faculty, and directors. The contents of data from each category were analyzed with the word frequency and the divergence indicator to identify the characteristics of each category. As a result, divergency indicator is effective to represent the differential factors of each category. This study provides word clouds of top 100 word with divergence indicator and top 100 common words of three categories with word frequency as a guide for users to write about a person in these categories and for editors to accept and monitor the biography from users.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.43-51
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• 2022

The purpose of this study is to provide basic information necessary for the establishment of mobile service marketing strategies, educational service development, and engineering education for Generation Z by analyzing the utilitization of various applications by Gen Z. To this end, 177 essays on mobile service usage experience were collected, major topics were analyzed using topic modeling, and these were visualized through word cloud analysis. As a result of the study, the main topics were related to 'transportation' such as movement and public transportation, 'personal management' such as schedule management, financial management, food management, 'transaction' such as checkout, meeting, purchase, 'leisure' such as eating out, travel, study, culture. Additionally, words such as time, thought, people, life, bus, information, confirmation, payment, KakaoTalk, and so on were found to have a high of frequency of use. Also, there was found to be a difference between topics by college. This study is meaningful in that it collected essays, which are unstructured data, and analyzed them through topic modeling.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.515-526
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• 2020

Digital healthcare is a combined area of medical field and IT and various information on digital healthcare is provided in social media. This study aims to find the research trend of digital healthcare by collecting and analyzing data related to digital healthcare through the social media. The data were collected from Naver and Daum's news and blogs from January 2008 to June 2019. Major keywords with high frequency were extracted and visualized with wordcloud and network analysis was used to analyze the relationship between major keywords. Research combining medical field and IT from 2008 to 2001, various convergence research based on medical field and IT from 2012 to 2015, convergence research that applied the 4th industrial revolution technologies such as big data, blockchain and AI were actively conducted from 2016 to June 2019.