• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.207-235
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• 2022

In a knowledge-based society where knowledge and information industries are the main pillars of the economy, knowledge sharing and diffusion and its systematic management are recognized as essential strategies for improving national competitiveness and sustainable social development. In the field of Information Systems (IS) research, where the convergence of information technology and management takes place in various ways, the evolution of knowledge occurs only when researchers cooperate in turning old knowledge into new knowledge from the perspective of the scientific knowledge network. In particular, it is possible to derive new insights by identifying topics of interest in the relevant research field, applied methodologies, and research trends through network-based interdisciplinary graftings such as citations, co-authorships, and keywords. In previous studies, various attempts have been made to understand the structure of the knowledge system and the research trends of the relevant community by revealing the relationship between research topics, methodologies, and co-authors. However, most studies have compared two or more journals and been limited to a certain period; hence, there is a lack of research that looked at research trends covering the entire history of IS research. Therefore, this study was conducted in the following order for all the papers (from its first issue in 1977 to the first quarter of 2022) published in the MIS Quarterly (MISQ) Journal, which plays a leading role in revealing knowledge in the IS research field: (1) After extracting keywords, (2) classifying the extracted keywords into research topics, methodologies, and theories, and (3) using topic modeling and keyword network analysis in order to identify the changes from the beginning to the present of the IS research in a chronological manner. Through this study, it is expected that by examining the changes in IS research published in MISQ, the developing patterns of IS research can be revealed, and a new research direction can be presented to IS researchers, nurturing the sustainability of future research.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.289-306
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• 2005

As FRBR defines structural framework based on ER modeling for bibliographic data elements, an effective tool is required to implement FRBR model. In this paper, we present the implementation of FRBR model based on Topic Maps. To show the effectiveness of Topic Maps as the implantation language of FRBR, we implement FRBR model of MyongSungHwangHu by means of Topic Maps. We can ascertain that topic-association of Topic Maps conceptually harmonize with entity-relation of FRBR, which means that Topic Maps is suitable for the implementation of FRBR model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.131-136
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• 2017

Recently, with the development of Internet media, many document materials have become exponentially increasing on the web. These materials are described, and the information on what is the most by this text are classified according. However, the text has meant that many have room for ambiguous interpretation must look at it from various angles in order to interpret them correctly. In conventional classification methods it was simply a classification only have the appearance of the text. In this paper, we analyze it in terms genetic algorithm and local preserving based techniques and implemented a clustering system fragmentation them. Finally, the performance of this paper was evaluated based on the implementation results compared to traditional methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1298-1301
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• 2013

대화 시스템에서 올바른 대화를 진행하기 위해서는 화자의 대화 의도를 파악하는 것이 중요하다. 특히 영어를 교육하기 위한 영어 교육용 대화 시스템에서는 학습자의 대화 의도 파악 오류가 발생할 경우 영어 교육에 문제가 발생하기 때문에 학습자의 대화 의도를 더욱 정확하게 분석 및 파악하는 것이 중요하다. 대화 패턴이란 시스템 발화에 대응되는 사용자 발화의 규칙적인 연쇄라고 할 수 있다. 대화 패턴 기반 대화 의도 부착 방법은 1) 대화 코퍼스 구축 2) 대화 시나리오에 있는 발화를 대상으로 기본 명사구 청킹(Base NP Chunking)을 하고 중심어(Head Word), 토픽 추적(Topic Tracking)에 의한 대화 패턴을 자동으로 추출한 후, 3) 대화 패턴 수동 검수이다. 대화 패턴 기반 대화 의도 부착 방법은 기본 명사구에 대한 지식만 가지고 있으면 대량으로 구축할 수 있다는 장점이 있다. 99 개의 대화 시나리오를 학습코퍼스로 하고 1 개의 대화 시나리오에 대해 대화턴 성공률을 시물레이션 한 결과 63.64%가 나왔다.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.171-187
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• 2019

The study aims to analyze the posts of depression-related Facebook groups to understand major topics discussed by group users. Specifically, the purpose of the study is to identify the topics and keywords of the posts to understand what users discuss about depression. Depression is a mental disorder that is somewhat sensitive in the online community, which is characterized by accessibility, openness and anonymity. The researchers have implemented a natural language-based data analysis framework that includes components ranging from Facebook data collection to the automated extraction of topics. Using the framework, we collected and analyzed 885 posts created in the past one year from the largest Facebook depression group. To derive more complete and accurate topics, we combined both automated and manual (e.g., stop words removal, topic size determination) methods. Results indicate that users discuss a variety of topics including depression in general, human relations, mood and feeling, depression symptoms, suicide, medical references, family and etc.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.419-426
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• 2018

Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.29-42
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• 2020

The purpose of this study is to present a qualitative research method for extracting and analyzing the comments written by YouTube video users. To do this, we used YouTube users' feedback to measure the hedonic, social, and utilitarian gratification of use and gratification theory(UGT) through by using analysis and topic modeling. The result of the measurement found that the first reason why users watch the trot singer, Cho Myung-sub's video in the KBS Korean broadcasting channel is to achieve hedonic gratification with high frequency. In word-document network analysis, the degree of centrality was high in words, such as 'cheering', 'thank you', 'fighting', and 'best'. Betweenness centrality is similar to the degree of centrality. Eigenvector centrality also shows that words such as 'love', 'heart', and 'thank you' are the most influential words of users' opinions. The results of the centrality analysis present that the majority of video users show their 'love', 'heart' and 'thank you' for the video. it indicates that the high words in centrality analysis is consistent with the high frequency words of hedonic and social gratification dimension of the UGT. The study has research methodological implication that shed light on the motivations for watching YouTube videos with UGT using text mining techniques that automate qualitative analysis, rather than following a survey-based structural equation model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.670-681
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• 2016

Recently, based on Internet and mobile environments, the Fintech industry that fuses finance and IT together has been rapidly growing and Fintech services armed with simplicity and convenience have been leading the conversion of all financial services into online and mobile services. However, despite the rapid growth of the Fintech industry, few studies have classified Fintech technologies into detailed technologies, analyzed the technology development trends of major market countries, and supported technology planning. In this respect, using Fintech technological data in the form of unstructured data, the present study extracts and defines detailed Fintech technologies through the topic modeling technique. Thereafter, hot and cold topics of the derived detailed Fintech technologies are identified to determine the trend of Fintech technologies. In addition, the trends of technology development in the USA, South Korea, and China, which are major market countries for major Fintech industrial technologies, are analyzed. Finally, through the analyses of networks between detailed Fintech technologies, linkages between the technologies are examined. The trends of Fintech industrial technologies identified in the present study are expected to be effectively utilized for the establishment of policies in the area of the Fintech industry and Fintech related enterprises' establishment of technology strategies.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.