• 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'.

• Journal of the HCI Society of Korea
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• v.6 no.1
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• pp.35-42
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• 2011

Mobile context-awareness becomes an important research topic since mobile information browsing is still difficult due to the limitations of mobile devices. On the other hand, it is easier to gather more user contexts because mobile devices are equipped with more sensors. In this paper, we introduce a method for detecting local hot topics from microblogs on a mobile device. In order to detect user-related topics from microblogs, it exploits mobile user contexts such as location, activity, blogging history and social relationship. Through taking advantage of these contexts, it retrieves user-related microblogs and also infers user interests. It can filter out unrelated topics based on the inferred interests. Based on our proposed method, a mobile user can be aware of topics related to interests surrounding the user.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.37-48
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• 2020

This study aimed at understanding trends of living lab studies and deriving implications for directions of the studies by utilizing text mining. The study included network analysis and topic modelling based on keywords and abstracts from total 166 thesis published between 2011 and November 2019. Centrality analysis showed that living lab studies had been conducted focusing on keywords like innovation, society, technology, development, user and so on. From the topic modelling, 5 topics such as "regional innovation and user support", "social policy program of government", "smart city platform building", "technology innovation model of company" and "participation in system transformation" were extracted. Since the foundation of KNoLL in 2017, the diversification of living lab study subjects has been made. Quantitative analysis using text mining provides useful results for development of living lab studies.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.19-28
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• 2017

Companies and Governments have Mainly used the Delphi Technique to Understand Research or Technology Trends. Because this Technique has the Disadvantage of Consuming a Large Amount of Time and Money, this Study Attempted to Understand and Predict Science and Technology Trends using the Topic Modeling Technique Latent Dirichlet Allocation (LDA). To this end, 20 Specific Artificial Intelligence (AI) Technologies were Extracted From the Abstracts of the US Patent Documents on AI. With Regard to the Extracted Specific Technologies, Core Technologies were Identified, and then these were Divided into Hot and Cold Technologies though a Trend Analysis on their Annual Proportions. Text/Word Searching, Computer Management, Programming Syntax, Network Administration, Multimedia, and Wireless Network Technology were Derived From Hot Technologies. These Technologies are Key Technologies that are Actively Studied in the Field of AI in Recent Years. The Methodology Suggested in this Study may be used to Analyze Trends, Derive Policies, or Predict Technical Demands in Various Fields such as Social Issues, Regional Innovation, and Management.

• Journal of The Korean Association of Information Education
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• v.25 no.4
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• pp.633-640
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• 2021

Education related issues are social problems in which various groups and situations are intricately linked to each other. It is difficult to find issues by analyzing social phenomena related to education. Korean based text analysis can be analyzed in a quantitative. With the development of text analysis techniques, research results have been recently achieved, and it can be fully utilized to derive educational issues from text data in Korean. In this study, petition articles in the field of childcare/education were collected on the online-board of the Blue House National Petition website, and text analysis was used to derive issues in the education world. The analysis derived 6 topics through Latent Dirichlet Allocation(LDA) among topic modeling techniques. The association rules of major keywords were analyzed and visualized as graphs. In addition to deriving educational issues through the existing questionnaire, it can provide implications for future research directions and policies in that issues can be sufficiently discovered through text-based analysis methods.

• Knowledge Management Research
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• v.22 no.3
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• pp.55-73
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• 2021

The sharing economy is considered as a collaborative consumption which enables customers to share unused resources. This study investigated the key factors affecting consumer loyalty in the context of sharing accommodation. Emotions, perceived value and self-image consistency were posited as key antecedents of enhancing customer loyalty. Authentic experience, home amenities, and price fairness were also considered as Airbnb's selection attributes. Airbnb was selected a survey target because it is the largest company in the domain of shared accommodation market. The research model was analyzed for 294 Airbnb customer through structural equation models. Additionally, this paper examine Airbnb customers' experiences by topic modelling method posted on the Naver blog. Based on the understanding of the key factors affecting customer loyalty to sharing accommodation, the analysis results contribute to establish effective marketing and operation strategies by enhancing customer experience.

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

• Korean Medical Education Review
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• v.24 no.2
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• pp.103-112
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• 2022

The purpose of this study was to explore, using topic modeling, the social value of doctors and medicine demanded by society as reflected in published newspaper articles in Korea. Ultimately, this study aimed to reflect social needs in the process of developing the Patient-Centered Doctor's Competency Framework in Korea. For this purpose, a total of 2,068 newspaper articles published from 2016 to 2020 were analyzed. Through topic modeling of these newspaper articles over the past 5 years, 18 topics were derived and divided into four categories. Focusing on the derived topics and keywords, the topics derived in specific years and the proportion of topics by year were analyzed. The results of this study make it possible to grasp the needs of society projected through the press for doctors and medicine. Due to the nature of the press, topics that frequently appeared in newspaper articles were mainly social phenomena related to requirements for doctors, particularly dealing with economic and legal aspects. In particular, it was confirmed that doctors are now required to have a wider range of competencies that go beyond their required medical knowledge and clinical skills. This study helped to establish doctor's competencies by analyzing social needs for doctors through the latest research methods, and the findings could help to establish and improve doctor's competencies through ongoing research in the future.

• Korean Journal of Culture and Social Issue
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• v.22 no.2
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• pp.285-311
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• 2016

This study analysed the published papers dealing with traffic behaviors since 2004 in south Korea. The following information was coded for each papers; year of publication, source, authors, main topic, and subtopic. The annual numbers of publication in 2004 and 2005 showed 6 articles and 7 articles. Since 2006, The annual numbers were increasing more than 10 papers. It means that the researches on traffic behavior were rich. The driver was main topic of 73.2% of articles. Cognition & Perception, Fatigue and Stress, and Alcohol were the main interest sub-topics dealing with main topic driver. Elderly driver was 10.4%, the interest in elderly drivers grew with population aging. And the dominant publications were Journal of traffic safety research, Journal of Korean Psychology Association, and Journal of the Koean Data Analysis Society with 60% of all articles for last 10 years.