• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Culinary science and hospitality research
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• v.23 no.3
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• pp.257-270
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• 2017

The purpose of this study was to visualize the semantic network with big data related to food exhibition at convention center. For this, this study collected data containing 'coex food exhibition/bexco food exhibition' keywords from web pages and news on Google during one year from January 1 to December 31, 2016. Data were collected by using TEXTOM, a data collecting and processing program. From those data, degree centrality, closeness centrality, betweenness centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of hospitality and destinations was high. In addition, the web visibility was also high for convention center programs, such as festival, exhibition, k-pop and event; hospitality related words, such as tourists, service, hotel, cruise, cuisine, travel. Convergence of iterated correlations showed 4 clustered named "Coex", "Bexco", "Nations" and "Hospitality". It is expected that this diagnosis on food exhibition at convention center according to changes in domestic environment by using these web information will be a foundation of baseline data useful for establishing convention marketing strategies.

• Educational Technology International
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• v.21 no.2
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• pp.193-216
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• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.533-547
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• 2018

The objective of this study is to examine the perception of golfwear and related trends based on major keywords and associated words related to golfwear utilizing big data. For this study, the data was collected from blogs, Jisikin and Tips, news articles, and web $caf{\acute{e}}$ from two of the most commonly used search engines (Naver & Daum) containing the keywords, 'Golfwear' and 'Golf clothes'. For data collection, frequency and matrix data were extracted through Textom, from January 1, 2016 to December 31, 2017. From the matrix created by Textom, Degree centrality, Closeness centrality, Betweenness centrality, and Eigenvector centrality were calculated and analyzed by utilizing Netminer 4.0. As a result of analysis, it was found that the keyword 'brand' showed the highest rank in web visibility followed by 'woman', 'size', 'man', 'fashion', 'sports', 'price', 'store', 'discount', 'equipment' in the top 10 frequency rankings. For centrality calculations, only the top 30 keywords were included because the density was extremely high due to high frequency of the co-occurring keywords. The results of centrality calculations showed that the keywords on top of the rankings were similar to the frequency of the raw data. When the frequency was adjusted by subtracting 100 and 500 words, it showed different results as the low-ranking keywords such as J. Lindberg in the frequency analysis ranked high along with changes in the rankings of all centrality calculations. Such findings of this study will provide basis for marketing strategies and ways to increase awareness and web visibility for Golfwear brands.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.894-901
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• 2013

This study is aim to research trend of private security and to suggest direction of improvement to sector of private security. This study has been to analyzed to be useful social network analysis(particularly Degree Centrality and Clossness Centrality) for using typical research method about trend of academic subject. As a result of Degree Centrality and Clossness Centrality, Individual factors such as Job Stress and Job satisfaction of private security are more keyword than institutional factors and policy factors such as Security Services Industry Act and training for Private Security guards. It means that research trend of private security are to study Individual factors rather than institutional factors and policy factors. But, this study is a limit as follows; First, An object of study is only to searching article in National Assembly Liberary. A follow-up studies are need to expand the range of an object of study for private security.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.151-166
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• 2020

Various experience keywords were derived through text mining analysis of two National Ecological and Cultural Exploration Routes. The results of this study were drawn as follows: The interaction between the experience keywords was analyzed by the degree centrality, closeness centrality, and betweenness centrality value calculated through the centrality analysis of the research site experience keywords. First, In the text mining analysis, 'walking' appeared as the top keyword in the I, II, and III periods of the two target areas. The keywords related to the stay type of "rental cottage" and "recreational forest" were derived for Masil Road in relation to accommodation facilities. However, the keywords related to the accommodation were not derived in Gubul Road. Second, as a result of the centrality analysis, the degree centrality of the keywords "walking", "sea", "look", "salt flats" of Masil Road and "walking", "lake" and "park" of Gubul Road was high. The keywords located at the center are "walking" and "sea" in the Masil Road, and "walking" in the Gubul Road. As an influential keyword, Masil Road is "experience" and Gubul Road is "history". Third, According to the results of the analysis, the keywords that appeared at the top of the Gubul Road are derived from the keywords related to the 1 ~ 8 course, and it is judged that the visitors are visiting the 1 ~ 8 course trail evenly. However, the Gubul Road only appears in the top keyword only for a few courses. Through this, it seems that three courses are intensively visited as the main course of 6 Gubul Road, 6-1 Gubul Road, and 8 Gubul Road.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.685-697
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• 2019

The study aimed to analyze the structure of China's passenger railway network in the years of 2008, 2013, and 2018, respectively. At the same time, it tried to investigate its derivative impact on the patterns of Chinese urban network. The analytical tool was based on the NetMiner4.0. In order to measure network characteristics of China's passenger railway network, it primarily focused on the degree centrality, betweenness centrality, and closeness centrality. First of all, the higher degree centralities, with a few exceptions, were observed in BeiJing, ShangHai, GuangZhou, WuHan, XiAn, ChengDu, HaErBin, and ShenYang over a decade. In contrast, the higher betweenness centralities were recorded in cities of higher development potential including WuLuMuQi, GuiYang, ShenYang, and KunMing. The closeness centrality analyses confirmed the fact that most metropoles like BeiJing, ShangHai, and GuangZhou kept the highest train accessibility during the same research period. At the same time, the opening up of a new stretch of high speed railway network has consecutively strengthened connectivity between BeiJing and TianJin. Owing to unprecedented development of railway traffic and its extensive operations, this study believes that Chinese major cities, without interruption, would pursue a series of urban policy alternatives geared towards railway stations-oriented networking and competitively try to extend their network ranges.

• Journal of Korean Library and Information Science Society
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• v.47 no.3
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• pp.95-114
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• 2016

The purpose of this preliminary study is to collect specific examples of book reports and understand semantic characteristics of them through semantic network. The analysis was conducted with 23 book reports which classified by three groups. The keywords were selected from the of book reports. Five types of keyword network were composed based on co-occurrence relations with keywords. The result of this study is following these. First, each keyword network of book reports of groups and individuals is shown to have different structural characteristics. Second, each network has different high centrality keywords according to the result analysis of 3 types of centrality(degree centrality, closeness centrality, betweenness centrality). These characteristic means that keyword network analysis is useful in recognizing the characteristics of not only groups' and but also individual's book reports.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.159-182
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• 2009

We construct product networks from the retail transaction dataset of an off-line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co-purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross-selling.