• Journal of Digital Convergence
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• v.15 no.2
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• pp.191-197
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• 2017

As individual opinions are expressed and discussed through SNS, SNS is used as a new basis to estimate the direction of public opinion. This change also appears in election. So many voters state their views through SNS, so that candidates utilize it as a new space for communication. In this paper, positive mention in SNS were collected and analysed in the course of the election of Korean 20th Congressman, to understand how the mention on election in SNS affects the result of election. This result was compared with the traditional survey on public opinion, to find out which one more corresponds to the result. In conclusion, mention in SNS coincide more with the result of elelction than the traditional survey.

• Journal of Fashion Business
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• v.21 no.4
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• pp.44-60
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• 2017

This study is to understand the image of the Korean beauty and rewards to be gained by trying to be a beautiful person and to study differences according to demographic characteristics. It was studied with the purpose of industrializing beauty image and selling it to foreign countries. The survey questionnaire was distributed to Seoul and Kyeongkido. Respondents totaled 301. Collected data were analyzed with frequency analysis, factor analysis, $X^2$-test, and regression. Results are ; (1) The external image of Korean beauty emphasizes round face, white skin, big eyes, double eyelids, round head shape, early twenties, tall, low body weight, thin waist, long neck, long legs, and thin fingers. (2) The inner image of the Korean beauty emphasizes mature personality, social economic ability, but not housework, and cultural artistic ability. (3) Rewards gained by trying to be a beauty are psychological, actual, and social ones. (4) External face and body image of the beauty are different by demographic characteristics (sex, age, marital status, final education, monthly average income, religion). (5) The inner image of the beauty is different by age, final education, and monthly average income. (6) Rewards gained by trying to be a beauty are different by sex, age, final education, and monthly average income.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1058-1073
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• 2022

Big data analytics and social media have shifted the way fashion trends are dictated. Fashion as a medium for expressing gender has created new concepts of masculinity in popular culture, where men are increasingly depicted in a softer style. In this study, we analyzed 2,879 menswear collections over a 10-year period from Vogue US to uncover key menswear trends. Using Semantic Network Analysis (SNA) on Orange3, we were able to quantitatively analyze how contemporary menswear designers interpreted diversified trends of masculinity on the runway. Frequency and degree centrality were measured to weigh the significance of trend keywords. "Jacket (f = 3056; DC = 0.80), shirt (f = 1912; DC = 0.60) and pant (f = 1618; DC = 0.53)" were among the most prominent keywords. Our results showed that soft masculine keywords, e.g., "lace, floral, and pink" also appeared, but with the majority scoring DC = < 0.10. The findings provide an insight into key menswear trends through frequency, degree centrality measurements, time-series analysis, egocentric, and visual semantic networks. This also demonstrates the feasibility of using text analytics to visualize design trends, concepts, and patterns for application as an ideation tool for academic researchers, designers, and fashion retailers.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.609-615
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• 2022

In this paper, we propose a monitoring system for illegal overseas direct purchase based on C2C transaction between individuals. The Customs Act stipulates that direct purchases from overseas are exempted from taxation only if they are less than a certain amount (US$150, but US$200 in the US) or are recognized as self-used goods. The act of reselling overseas direct purchase items purchased with exemption from taxation online, etc., is a crime of smuggling without a report. Nevertheless, the number of re-sells on online second-hand websites is increasing, and it is becoming a controversial social issue of continuous violation of the Customs Act. Therefore, this study collects unspecified transaction details related to overseas direct purchase, refines the data in a big data method, and designs it as a monitoring system through natural language processing, etc. analyzed. It will be possible to use it to crack down on illegal transactions of overseas direct purchase goods.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.606-615
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• 2015

This study aims to provide suggestions for the development of educational contents on historical events that can solve the existing curriculum's problems, such as the disproportionate weight given to Western historical events. The study focuses on content ranging from the start of the Agricultural Revolution (7000 BC.) to the start of the Industrial Revolution (AD. 1760). The results are as follows. We used the Delphi technique for deriving global historical events. Among them, 56 historical events were selected as the data for Social Network Analysis (SNA). The results of SNA showed that topics related to Civilization has a high priority. In addition, the results of a coagulation analysis showed the events can be divided into seven groups. The classification criteria is different from the criteria used for the current period. We expect that the suggested framework developed for historical contents will constitute a new approach to historical interpretation through network visualization and linkage analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.127-129
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• 2015

A meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result of integrating and analyzing many quantitative research results. This study will find meaningful independent variables for criterion variables that affect influencing on purchase intention in social network service, on the basis of the results of a meta-analysis. We reviewed a total of 29 studies related on purchase intention in social network service published in Korean journals between 2000 and 2015, where a cause and effect relationship is established between variables that are specified in the conceptual model of this study. Thus, we present the theoretical and practical implications of these results and discuss the differences between these results through a comparative analysis with previous studies.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.115-125
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• 2021

This study aims to investigate the effects of self-service technology (SST) characteristics and consumer characteristics on consumer resistance and satisfaction with SST in restaurants. An online survey was conducted for consumers in their 20s and 50s who used SST at restaurants, and 343 data were used for analysis. As a result, convenience and tech-controllability have a negative effect on consumer resistance with SST, whereas complexity, social risk and relationship orientation have a positive effect. In addition, convenience, entertainment, and tech-controllability have a positive effect on consumer satisfaction with SST, whereas social risk and relationship orientation have a negative effect. This study contributes practically and academically in that it proposes a practical strategy to reduce consumer resistance and increase satisfaction, and identifies the determinants of consumer response to SST. In future studies, an in-depth analysis of consumers' ambivalent responses to SST is required.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.69-74
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• 2012

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.

• Korean Journal of Social Welfare Studies
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• v.49 no.4
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• pp.65-100
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• 2018

Korean Youth are facing diverse problems. For-instance Korean youth are even called '7 given-up generation' which indicates that they gave up marriage, giving birth, social relationship, housing, dream and the hope. From this point, the study concludes that the influential factors of the alcohol problems of youth should be studied based on the eco social perspectives. And it adopted data-mining methods, using SAS-Enterprise Miner for the analysis, targeting 2538 youths. Specifically, the study analyzed and chose the most predictable model using decision tree analysis, artificial neural network and logistic analysis. As the result, the study found that gender, age, smoking, spouse, family-number, jobsearching and economic participation are statistically significant determinants of alcohol problems of youth. Precisely, those who are male, younger, have the spouse, have less family number, searching jobs, have more income and have the job were more prone to have the alcohol problems. Based on the result, this study proposed the addiction problems targeting youth and etc. and expect to have the contribution on implementing procedures for the alcohol problems.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.