• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.72-78
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• 2021

Currently, the Korean wave is not limited to popular culture, but has a significant impact not only on Korea's national image but also on the improvement of Korean companies' products and image of Korea. In this study, using Textom to confirm the change in foreign tourists' image of Korea, the data collection period was 1 year of 2020, when COVID 19 occurred, as a collection period for "Korea and foreigner" and related key words, each Hallyu content, and ranked in the top 80 keywords were derived. Centrality analysis for semantic network visualization was performed using UCINET6, and through CONCOR analysis, 7 groups 'K-Quarantine ', 'K-Drama', 'K-Movie', 'K-Beauty', 'K-Shopping', It was clustered into 'K-Tech' and 'K-Pop'. As a result of the analysis, the image of Korea abroad generally recognized the Korean Wave as cultural content, but after the outbreak of COVID 19, it is judged that it has been recognized as a country with a successful case of K-Quarantine.

• Journal of Korea Game Society
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• v.19 no.5
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• pp.63-76
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• 2019

The purpose of this study is to identify how game players experience games artistically. We organized indicator categories related to artistic experiences in games through play report produced in participatory player workshop. Based on this indicator, we embodied the artistic meaning of game experience by performing semantic network analysis. As a result, we verified that games can offer opportunities for players to reflect on themselves and their lives.

• Korean Journal of Childcare and Education
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• v.16 no.6
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• pp.253-272
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• 2020

Objective: This study aimed to analyze the counseling contents of daycare center teachers by using text mining and semantic network analysis methods to find the necessary support directions for daycare teachers and to improve the quality of child-care. Methods: Five hundred thirteen cases of counseling recorded on the open bulletin board of online counseling (Naver Bands for Nursery Teacher Counseling) were collected, and frequency analysis, centrality solidarity analysis, and machine learning-based topic analysis were conducted using the NetMiner4.3 program. Results: First, 'teacher-to-child ratio' was highest in the frequency. Second, 'colleagues' were all high in all centrality analysis. Third, machine learning-based topical analysis shows that the topics were categorized as subjects about 'childcare and education', 'working environment that supports professional development' and 'working condition', and among them, 'first-time teacher concerns' accounted for 44% of the total counseling content. Conclusion/Implications: This study implied that it is necessary to provide high-quality child-care and education to infants by lowering the 'teacher-to-child ratio', and a systematic program is needed to help improve effective communication skills in interpersonal relationships such as between parents, fellow teachers, and principals. In addition, self-development and efforts to improve teachers expertise should be prioritized in order to improve infant care quality and quality of teachers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• The Research Journal of the Costume Culture
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• v.29 no.4
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• pp.603-615
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• 2021

The aim of this study was to analyze the semantic network structure of keywords and the visual composition of images extracted from Instagram in relation to the multi-persona phenomenon with in fashion imagery, which has recently been attracting attention. To this end, the concept of a 'secondary character', which forms a separate identity from a 'main character' on various social media platforms as well as on the airwaves, was considered as the spread of multi-persona and #SecondaryCharacter on Instagram was investigated. 3,801 keywords were collected after crawling the data using Python and morphological analysis was undertaken using KoNLP. The semantic network structure was then examined by conducting a CONCOR analysis using UCINET and Netdraw to determine the top 50 keywords. The results were then classified into a total of 6 clusters. In accordance with the meaning and context of the keywords included in each cluster, group names were assigned : virtual characters, relationship with the main character, hobbies, daily record, N-job person, media and marketing. Image analysis considered the technical, compositional, and social styles of the media based on Gillian Rose's visual analysis method. The results determined that Instagram uses fashion images that virtualize one's face to produce multi-persona representation s that show various occupations, describe different types of hobbies, and depict situations pertaining to various social roles.

• Journal of Contemporary Eastern Asia
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• v.19 no.2
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• pp.234-263
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• 2020

This study aims to assess the quality of the EU's public diplomacy efforts through social media. Specifically, this research explores the headquarters of European public diplomacy (European External Action Service, EEAS) and its subordinate agent (EU Delegation to South Korea, EUDK). The main research question is "how coherently and effectively does the EU execute public diplomacy strategies in the third countries?'' This study employs both quantitative and qualitative approaches―the former for the results presentation and the latter for the results interpretation. The results are summarized in two points. First, both the EEAS and the EUDK maintained an objective position by posting only neutral perspectives on their social media. Second, the narratives of the main actors were the same, but the main themes were different according to their contexts. The analysis discusses the effectiveness of the EU's strategic narratives by focusing on measuring message reception. Its limited findings were that social media postings could not induce the audiences' active discussion of the EU's diplomatic performances. The conclusion suggests that the EU should identify the expectations from its global counterparts and use these to forge the Union's strategic narratives.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.271-279
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• 2022

In the discipline of design science, the integration of cognitive, semantic, and affective elements is crucial in the conception and development of a product. Affective components in IT artefacts have attracted researchers' attention, but little attention has been given to Geographic Information Systems (GIS). This research was conducted to identify emotions in web-based GIS, and determine design influences on the emotions using Kansei engineering (KE). In the evaluation procedure, ten web-based GIS were used as specimens, and 20 Kansei words were used as emotional descriptors in the Kansei checklist. 50 participants were asked to rate their emotional responses towards the specimens on the Kansei checklist. Principal Component Analysis was used to discover the semantic structure of Kansei, in which dynamism and spaciousness were identified. Significant Kansei concepts were identified using Factor Analysis, in which dynamic & well-organized, refreshing, spacious, professional, and nautical-look were identified. Partial Least Square analysis has assisted the research in discovering the significant design influence to the Kansei. These findings provide designers and other stakeholders with valuable knowledge for strategizing future web-based GIS designs that incorporate user emotions.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.772-788
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• 2022

This study investigates whether the image of the casual Hanbok brand is being communicated to consumers successfully. We conducted a semantic network analysis to identify ways of revitalizing communication between casual Hanbok brands and consumers; in addition, we quantitatively evaluated the effectiveness of communication marketing through Quadratic Assignment Procedure (QAP) analysis. Unstructured data from 2014-2021 were collected through portal sites and then refined and networked. Our analysis showed that casual Hanbok brands generally target younger people and that different brands employ similar methods to promote and popularize the casual Hanbok style. Consumers tended to recognize and show interest in casual Hanbok, suggesting the potential to expand the market to Blue Ocean. However, some of our findings revealed the potential factors of style coordination risk and prejudice against existing Hanbok, which could potentially hinder casual Hanbok's uptake and adoption. We conclude that increasing the demand for casual Hanbok depends not only on delivering an accurate brand image to consumers but also on balancing fashion with traditional images when planning products and providing styling information.

• Journal of Hydrogen and New Energy
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• v.33 no.2
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• pp.121-129
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• 2022

With the advent of the post-2020 climate regime, the hydrogen industry is growing rapidly around the world. In order to build the hydrogen economy, it is important to identify social issues related to hydrogen and prepare countermeasures for them. Accordingly, this study conducted a semantic network analysis on hydrogen news from NAVER. As a result of the analysis, the number of hydrogen news in 2020 increased by 4.5 times compared to 2016, and as of 2018, the hydrogen issue has shifted from an environmental aspect to an economic aspect. In addition, although the initial government-led hydrogen industry is expanding to the mobility field such as privately-led fuel cell electric vehicles and hydrogen fuel, terms showing concerns about the safety such as explosions are constantly being exposed. Thus, it is necessary not only to expand the hydrogen ecosystem through the participation of private companies, but also to promote hydrogen safety.

• The Research Journal of the Costume Culture
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• v.32 no.1
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• pp.38-57
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• 2024

This study utilizes social big data to investigate the factors influencing the awareness, attitude, and behavior toward vegan fashion consumption among global and Korean consumers. Social media posts containing the keyword "vegan fashion" were gathered, and meaningful discourse patterns were identified using semantic network analysis and sentiment analysis. The study revealed that diverse factors guide the purchase of vegan fashion products within global consumer groups, while among Korean consumers, the predominant discourse involved the concepts of veganism and ethics, indicating a heightened awareness of vegan fashion. The research then delved into the factors underpinning awareness (comprehension of animal exploitation, environmental concerns, and alternative materials), attitudes (both positive and negative), and behaviors (exploration, rejection, advocacy, purchase decisions, recommendations, utilization, and disposal). Global consumers placed great significance on product-related information, whereas Korean consumers prioritized ethical integrity and reasonable pricing. In addition, environmental issues stemming from synthetic fibers emerged as a significant factor influencing the awareness, attitude, and behavior regarding vegan fashion consumption. Further, this study confirmed the potential presence of cultural disparities influencing overall awareness, attitude, and behavior concerning the acceptance of vegan fashion, and offers insights into vegan fashion marketing strategies tailored to specific cultures, aiming to provide vegan fashion companies and brands with a deeper understanding of their consumer base.