• Korean Institute of Interior Design Journal
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• v.19 no.5
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• pp.95-104
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• 2010

One of 21st century's main paradigms is a 'Culture', and people started to express their 'cultural desires and demands' regarding public environments. Accordingly central and local governments are paying attention to these demands from their citizens and trying to establish the policies to meet these needs. This research is done to suggest the strategy programming model to support the executions of culture-centered public designs which are based on the local resources and identities. The entire research contains three sub-topics. First topic is setting the prototype phases of strategy programming. Second topic is analyzing the associated indices for each prototype phase. Third topic is suggesting Matrix Model in which the prototype phases and their associated indices are linked. Among three topics, this paper deals with the first one - the prototype phases of strategy programming. It studies this subject in relation with Place Marketing which emphasizes the local resources and identities. The prototype phases are comprised of 3 steps for Place Strategy and 4 steps for Marketing Strategy. Place Strategy should be considered prior to Marketing Strategy because in culture-centered public design, locality has priority over other concerns. The phases for Place Strategy includes 'Resource_analyzing of local resources', 'Mission_setting a purpose', and 'Targeting_segmenting target groups'. The phases for Marketing Strategy involves 'Organization_instituting the main body and/or partnership', 'Image Positioning_setting an unique local image', 'Point_realizing the product', and 'Channel_deciding the sales promotion tools'.

• Knowledge Management Research
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• v.20 no.1
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• pp.45-75
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• 2019

Recently, the growth of beauty industry such as plastic surgery and beauty is continued every year in Korea. With the increased interest in appearance based on the improvement of life standard and the development of media, people's perception of cosmetic plastic surgery is changing. Now, as the service for consumer satisfaction based on their desire, the perception of plastic surgery medical service is changed to the high value-added industry with the high growth potential. Thus, this study aims to suggest the strategies for providing the medical service that could satisfy customers, by drawing the factors cognized as important when customers aim to get the cosmetic plastic surgery, and then additionally analyzing the relationships of those factors. On top of performing the topic modeling based on customers' comments data of social commerce related to cosmetic plastic surgery, this study also conducted the network analysis for visualizing the relations of each keywords. The drawn main factors were divided by applying the sub-categories of the SERVQUAL theory, and the additional characteristics of plastic surgery were shown by referring the relevant previous researches. Moreover, the interview with the cosmetic plastic surgery specialists (plastic surgeons) and customers who actually received the plastic surgery, helped the understanding of the interpretation of each factor and the actual relevant phenomenons. The significance of this study is to draw and discuss the main factors that should be observed by Korean cosmetic plastic surgery medical institutes, by mining and analyzing the opinions of customers interested in the cosmetic plastic surgery and procedure with the use of topic modeling. In other words, the quality of medical service of cosmetic plastic surgery could be improved by presenting the key factors that could be considered by the cosmetic plastic surgery medical service suppliers and also the actual strategies.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.47-56
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• 2021

The purpose of this study is to use network analysis to identify trends in university personality-related studies and provide implications for future research directions. For the purpose of this study, 194 papers related to personality of university students published in Korean scholarly journals. First, research began to be published in 2004, slightly increased in 2012, continued an upward curve from 2015, peaked in 2017, and is confirmed to be a downward trend. Second, the main keywords with the centrality analysis were 'society' and 'cultivation'. Third, keywords on the cognitive side and individual dimension of personality in the first period (2004 - 2010), social dimension and emotional side of personality in the second period (2011-2015), and social level and cognitive, emotional, and behavioral aspects of personality in the third period (2016-2020). Fourth, Topic 2 consisted of keywords of ability, life, interpersonal, satisfaction, and adaptation, and Topic 1 consisted of competence, morality, citizens, society, and practice. Fifth, Topic 4 alone in the first period, in the order of Topic 1 and Topic 2 in the second period, and in the order of Topic 2 and Topic 1 in the third period.

• Korean Journal of Childcare and Education
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• v.16 no.1
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• pp.21-49
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• 2020

Objective: The purpose of this study was to understand the research trends of inclusive child-care for young children with disabilities and suggest some implications for future research on inclusive child-care. Methods: There were a total 330 previous research papers about child-care inclusion, which means the practice of educating and caring for children aged 0 to 5 years old with disabilities alongside their typically developing peers at child-care centers. The studies were analyzed in terms of main research topic, subject, and research methods. Results: The major results of this study were as follow. First, a quantitative increase of the research since 2003 and a sharp increase of total research in this area since 2006 were found. Second, as a result of the main topic analysis, recognition and attitude research was the most frequently conducted, followed by the research about child-care programs, teacher and family support, and the effect of inclusive child-care. Third, more than half of the researches were done by teachers. Lastly, quantitative research methods were mainly used. Conclusion/Implications: Based on these findings, this study suggested to extend the research on inclusive child-care effect, intervention programs, non-disabled children, infants, and qualitative research.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.4
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• pp.402-411
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• 2020

Purpose: The purpose of this study is to explore the trends of research related to ethical topics in Korean nursing students. Methods: A total of 131 articles that were published from 2000 to 2020 were analyzed and summarized according to publishing type, research design, subject, data analysis method, main research topic, research variables and instrument. Results: Most studies were journal articles (93.9%) and their most frequent research design was survey (75.7%). The research subjects covered all grades (35.1%) or they were divided between clinical nursing practicum (29.8%) or not (21.4%). The main research topics were biomedical ethics, ethical values, moral judgment and ethics education. Recently, ethical decision making and practical ability in nursing practice were reported. The instruments for measuring variables were limited and the same tools were used several times. Conclusion: Based on the findings of this study, it is suggested that the selection of various research topics and the application of research methods related to ethics for nursing students will continue in response to rapidly changing social phenomena in the future. In particular, it is necessary that research related to ethical and practical ability as well as ethical attitudes and perceptions of nursing students be actively carried out.

• Child Health Nursing Research
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• v.27 no.3
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• pp.201-210
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• 2021

Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI). Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling. Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life". Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

• Journal of the Korean Society of Food Culture
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• v.37 no.3
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• pp.196-212
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• 2022

This study examines the trend of research on food and culture in papers published in the Journal of The Korean Society of Food Culture from 1986 to 2020. The journals published a total of 329 papers, which we classified into 5 main categories and 13 middle categories. Of these, 204 articles were on "Korean traditional food culture." The most studied topic in the entire period was "Perception of Koreans towards traditional food, preference, satisfaction, and usage." A total of 76 studies related to "Korean contemporary food culture." The most advanced topic researched concerned "Recognition and attitude"; these studies were consistently carried out throughout the research period. The main classification of "World food culture" encompassed 32 studies, with major research focused on "World's Modern Food Culture" and the most advanced being "Comparison of Food Cultures of Foreign and Korean Food Cultures." All studies were consistently spaced out during the study period. These studies provide an integrated knowledge in the field of food and culture and can be used as a basic material for related research in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.140-149
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• 2018

Social data such as users' comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users' hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users' interests on different topics.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.79-100
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• 2018

Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summarization techniques by surveying recent evaluation methodologies. At the end of the survey, we emphasize on both current and future research challenges in this domain through a level of depth analysis of the most recent summarization approaches.