• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.180-191
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• 2016

This study utilized a semantic network analysis for Korean major newspaper articles concerning 'Bernie Sanders'. 'Bernie Sanders' promotes conservative values of 'Americana' as well as the progressive values of 'relieving inequality', and thus, perhaps he is a subject on which ideological differences between the press can be distinctively manifest. Upon comparison of the priority of frequency between the conservative press and progressive press, the conservative press frequently used the expressions, 'socialist' and 'black man', whereas the progressive press frequently used the expressions, 'inequality' and 'problem'. Both the conservative press and progressive press displayed particularly different semantic compositions with the term, 'Korea'. The progressive press aimed to express the criticism of social problems and established politics identified by Sanders in relation to the 'Korean' society, whereas the conservative press criticized the blunt expressions stating that a specifically named politician resembles Sanders, and the specific party and term of 'Korea'. A completely different disposition of reports from different perspectives and context was ascertained, regardless of the use of the same terms. Thus, it is demonstrated that the semantic composition of the press on a specific issue displays significant differences according to their ideological disposition.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.1-11
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• 2009

This study presents HCWS algorithm for researcher grouping on a weighted researcher network. The weights represent intensity of connections among researchers based on the number of co-authors and the number of co-authored research papers. To confirm the validity of the proposed technique, this study conducted an experimentation on about 80 research papers. As a consequence, it is proved that HCWS algorithm is able to bring about more realistic clustering compared with HCS algorithm which presents semantic relations among researchers in simple connections. In addition, it is found that HCWS algorithm can address the problems of existing HCS algorithm; researchers are disconnected since their connections are classified as weak even though they are strong, and vise versa. The technique described in this research paper can be applied to efficiently establish social networks of researchers considering relations such as collaboration histories among researchers or to create communities of researchers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1442-1453
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• 2016

Now, there are prodigiously expanding worldwide economic networks in the information society, which require their social structural changes through technology innovations. This paper so tries to formally define a process-centered knowledge model to be used to analyze policy-making procedures on technology innovations. The eventual goal of the proposed knowledge model is to apply itself to analyze a topic network based upon composite keywords from a document written in a natural language format during the technology innovation procedures. Knowledge model is created to topic network that compositing driven keyword through text mining from natural language in document. And we show that the way of analyzing knowledge model and automatically generating feature keyword and relation properties into topic networks.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.24-33
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• 2021

This study examined a semantic relationship expressed in college presidents' welcome greetings in order to explore the promotion strategies and future direction of universities in Daegu & Gyeongbuk provinces in South Korea. Greetings were collected from university websites as of September, 2020. According to word frequency analysis, "everyone," "welcome," and "visiting" were mostly used in the headlines. In the body texts, "college" and "education" were frequently paired. While the two- & three-year colleges focus on industrial and technical capabilities, four-year universities tend to emphasize educational excellence and academic research performance. This study is valuable in that it understands the direction that universities in Daegu and North Gyeongsang Province put forward amid the decreasing school-age population and the changing social environment.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.28-48
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• 2019

This research is aimed at identifying the perception associated with urban regeneration and predicting policy implications of future directions by analyzing the trend of urban regeneration depicted in the mass media by utilizing SNA (Semantic-Network Analysis) techniques. As the number of articles has increased, it is noted through analysis that the interrelationships between social phenomena and issues have combined to form the meaning of urban regeneration. Overall, 'urban' and 'regeneration' keywords also appeared at different periods, with 'urban' closely related to 'regeneration' starting in 1970 when urbanization was becoming more prevalent. It was analyzed that the frequency of 'urban' appeared more frequently in the early 1990s, while the frequency of 'rural' decreased sharply. Until the 1990s, the slums and the recession that appeared as side effects of urban problem-solving policies were mostly concentrated in cities. Policy discussions were conducted with the goal of improving the physical environment of cities rather than concentrating on the surrounding rural areas. The distributions of the keywords 'development' and 'regeneration' have increased quantitatively since the 1970s, and urban polarization has exploded due to the development of the external growth of cities, mirroring the trend of accelerated environmental threats. In particular, the keywords for 'regeneration' emerged mainly related to environmental problems, which led to the need for urban regeneration, and environmentally and ecologically friendly development. The emergence of "urban," "regeneration" and "environment" as keywords having to do with urban regeneration grew in the 1990s. This suggests that urban regeneration is now linked to "environment", as that has become a social issue.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.363-366
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• 2016

추천 기법은 개인의 관심사와 상황을 고려한 개인화된 아이템을 제공함으로써 아이템의 소비과정에서 발생하는 부하를 줄여주고 정보 소비의 효율성을 증대시키는데 중요한 역할을 한다. 본 연구에서는 전통적인 추천 기법인 Content-Based(CB)기법과 최근 온라인 소셜 네트워크의 경향을 반영한 Social Network-based(SN)기법을 접목하여 새로운 복합방식의 정보 추천 기법을 제시한다. CB 기법의 대표적인 한계점인 cold start problem과 SN 기법의 추천 아이템의 전문성 문제를 상호 보완하며, 특히 최근 소셜 네트워크의 특징인 비신뢰 (non-trust) 기반의 영향력 있는 정보 확산자가 존재하는 환경에서 기법을 적용할 수 있도록 하였다. 또한 대부분 사람 추천 중심인 기존의 SN 기법들과는 달리 사람에게 제공할 정보의 추천에 초점을 두며, 정보 선정과정에서 개인의 온라인과 현실(real world)에서의 사회 활동 정보를 모두 활용하여 더육 더 개인화된 가치 정보를 제공하고자 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.193-200
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• 2021

Recently, posts and opinions on tourist attractions are actively shared on social media. These social big data provide meaningful information to identify objective images of tourist destinations recognized by consumers. Therefore, an in-depth understanding of the tourist image is possible by analyzing these big data on tourism. The study is to analyze destination images in Gangwon-do using big data from social media. It is wanted to understand destination images in Gangwon-do using semantic network analysis and then provided suggestions on how to enhance image to secure differentiated competitiveness as a destination for tourists. According to the frequency analysis results, as tourism in Gangwon-do, Sokcho, Gangneung, and Yangyang were mentioned at a high level in that order, and the purpose of travel was restaurant tour, gourmet food, family trip, vacation, and experience. In particular, it was found that they preferred day trips, weekends, and experiences. Four suggestions were made based on the results. First, it is necessary to develop various types of hotels, accommodation facilities and experience-oriented tour packages. Second, it is necessary to develop a day-to-day travel package that utilizes proximity to the Seoul metropolitan area. Third, it is necessary to promote traditional restaurants and local food. Finally, it is necessary to develop tourist package suitable for healing and family travel. Through this research, the destination image of Gangwon-do was identified and a tourism marketing strategy was presented to improve competitiveness. It also provided a theoretical basis for the use of the big data of tourism consumers in the field of tourism business.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.1-10
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• 2021

This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.