• Journal of Fashion Business
• /
• v.23 no.3
• /
• pp.85-100
• /
• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• Journal of Korean Academy of Nursing
• /
• v.51 no.1
• /
• pp.68-79
• /
• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.1
• /
• pp.537-544
• /
• 2022

As the penetration of electric vehicles and development of new models, user experience factors are getting more important in designing connectivity systems for car infotainment services. The primary object of this study is to identify commonalities and differences by comparing user experience factors in the Korean and US electric vehicle markets. This study derived connectivity keywords by text mining the vehicle introduction on the market in each country, and performed centrality, cluster analysis and visualization mapping using the semantic network analysis. As a result, the Korean new electric vehicle connectivity service mainly focused on driving functions such as driving, parking assistance, and charging, while US focused on device connection, convenience function control, app use, entertainment viewing. Based on the analysis, this study presented the practical implications in marketing, system design, and HMI design.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.5
• /
• pp.1-14
• /
• 2020

This study conducted a topic modeling and semantic network analysis of 'korean wave' and its meaning in Korean society from 2000 to 2019 by applying an agenda setting theory. For this purpose, a total of 197,992 newspaper articles which reported 'korean wave' issues were analyzed by applying topic modeling and semantic network analysis. As a result, first, the word 'korean wave' mainly appeared in korean-related regions in the korean press. culture and economy. second, a total of 9 topics related to korean wave issues appeared. This was followed by 'broadcast', 'export', 'domestic and foreign affairs', 'education', 'beauty and fashion', 'music and performance', 'tourism', 'media(platform)', and 'region'. Lastly, korean wave was mainly discussed at the cultural and economic ares. In addition, it was clustered into five characteristics: 'cultural hallyu', 'business hallyu', 'education', 'environment', and 'geography'.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3976-3990
• /
• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• Journal of The Korean Association of Information Education
• /
• v.25 no.6
• /
• pp.935-945
• /
• 2021

The purpose of this study is to analyze the social perception of Arduino lecture using big data analysis method. For this purpose, data from January 2012 to May 2021 were collected using the Textom website as a keyword searched for 'arduino + lecture' in blogs, cafes, and news channels of NAVER website. The collected data was refined using the Textom website, and text mining analysis and semantic network analysis were performed by opening the Textom website, Ucinet 6, and Netdraw programs. As a result of text mining analysis such as frequency analysis, TF-IDF analysis, and degree centrality it was confirmed that 'education' and 'coding' were the top keywords. As a result of CONCOR analysis for semantic network analysis, four clusters can be identified: 'Arduino-related education', 'Physical computing-related lecture', 'Arduino special lecture', and 'GUI programming'. Through this study, it was possible to confirm various meaningful social perceptions of the general public in relation to Arduino lecture on the Internet. The results of this study will be used as data that provides meaningful implications for instructors preparing for Arduino lectures, researchers studying the subject, and policy makers who establish software education or coding education and related policies.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.211-218
• /
• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.5
• /
• pp.129-153
• /
• 2023

This study was conducted in the context of the Covid-19 pandemic by analyzing a large amount of press report frames regarding the Covid-19 vaccine which is of great public interest, in order to explore the role and direction of trusted media as core elements of crisis communication. The study period lasted for eight months beginning in November 2020 when the development of the Covid-19 vaccine was in progress until June 2021. Set-up as research subjects were the Chosun Ilbo, Joongang Ilbo, Dong-A Ilbo and Hankyoreh according to their public confidence rankings and number of readers.The analysis method used structured topic Modeling (STM) and semantic network analysis. As a result, based on a clear cluster of word structures and a central analysis value, a total of 64 relevant frames, 16 for each news company, were gathered. In the third phase a comparative analysis of the four news companies was carried out to verify the organizational degree of the frames and substantial differences.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.4
• /
• pp.402-409
• /
• 2006

In recent years, several methods in distributed environment have been proposed in which a user-centered multimedia service may be efficiently provided. However, problems such as the improvement of QoS, streaming and the dynamic service of data for distributed service of multimedia data are introduced. In this paper we propose $POX -H_{r}$ structure for user-centered multimedia service in distributed network environment. The proposed $POX -H_{r}$ structure are constructed by disjunct, conjunct, semantic and filtering mapping scheme, and its structure are updated by $M_{filtering}$ scheme. The comparison results shows that the proposed method provides the better than the other methods.

• The KIPS Transactions:PartD
• /
• v.13D no.2 s.105
• /
• pp.147-156
• /
• 2006

Event logging plays increasingly an important role in system and network management, and syslog is a de-facto standard for logging system events. However, due to the semi-structured features of Common Log Format data most studies on log analysis focus on the frequent patterns. The extensible Markup Language can provide a nice representation scheme for structure and search of formatted data found in syslog messages. However, previous XML-formatted schemes and applications for system logging are not suitable for semantic approach such as ranking based search or similarity measurement for log data. In this paper, based on ranked keyword search techniques over XML document, we propose an XML tree structure through a new data modeling approach for syslog data. Finally, we show suitability of proposed structure for semantic retrieval.