• 한국의류학회지
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• 제46권6호
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• pp.1004-1022
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• 2022

This study seeks to investigate K-fashion's external image by examining the trends in global media reporting. It applies Dynamic Topic Modeling (DTM), which captures the evolution of topics in a sequentially organized corpus of documents, and consists of text preprocessing, the determination of the number of topics, and a timeseries analysis of the probability distribution of words within topics. The data set comprised 551 online media articles on 'Korean fashion' or 'K-fashion' published on Google News between 2010 and 2021. The analysis identifies seven topics: 'brand look and style,' 'lifestyle,' 'traditional style,' 'Seoul Fashion Week (SFW) event,' 'model size,' 'K-pop,' and 'fashion market,' as well as annual topic proportion trends. It also explores annual word changes within the topic and indicates increasing and decreasing word patterns. In most topics, the probability distribution of the word 'brand' is confirmed to be on the increase, while 'digital,' 'platform,' and 'virtual' have been newly created in the 'SFW event' topic. Moreover, this study confirms the transition of each K-fashion topic over the past 12 years, along with various factors related to Hallyu content, traditional culture, government support, and digital technology innovation.

• 산업경영시스템학회지
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• 제40권4호
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• pp.46-54
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• 2017

'Consulting', which is the main research topic of the knowledge service industry, is a field of study that is essential for the growth and development of companies and proliferation to specialized fields. However, it is difficult to grasp the current status of international research related to consulting, mainly on which topics are being studied, and what are the latest research topics. The purpose of this study is to analyze the research trends of academic research related to 'consulting' by applying quantitative analysis such as topic modeling and statistic analysis. In this study, we collected statistical data related to consulting in the Scopus DB of Elsevier, which is a representative academic database, and conducted a quantitative analysis on 15,888 documents. We scientifically analyzed the research trends related to consulting based on the bibliographic data of academic research published all over the world. Specifically, the trends of the number of articles published in the major countries including Korea, the author key word trend, and the research topic trend were compared by country and year. This study is significant in that it presents the result of quantitative analysis based on bibliographic data in the academic DB in order to scientifically analyze the trend of academic research related to consulting. Especially, it is meaningful that the traditional frequency-based quantitative bibliographic analysis method and the text mining (topic modeling) technique are used together and analyzed. The results of this study can be used as a tool to guide the direction of research in consulting field. It is expected that it will help to predict the promising field, changes and trends of consulting industry related research through the trend analysis.

• 한국항공운항학회지
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• 제31권3호
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• pp.42-49
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• 2023

Not only in aviation industry but also in other industries, safety data plays a key role to improve the level of safety performance. By analyzing safety data such as aviation safety report (text data), hazard can be identified and removed before it leads to a tragic accident. However, pre-processing of raw data (or natural language data) collected from each site should be carried out first to utilize proactive or predictive safety management system. As air traffic volume increases, the amount of data accumulated is also on the rise. Accordingly, there are clear limitation in analyzing data directly by manpower. In this paper, a topic prediction model for aviation safety mandatory report is proposed. In addition, the prediction accuracy of the proposed model was also verified using actual aviation safety mandatory report data. This research model is meaningful in that it not only effectively supports the current aviation safety mandatory report analysis work, but also can be applied to various data produced in the aviation safety field in the future.

• 산경연구논집
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• 제13권9호
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• pp.37-50
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• 2022

Purpose: The purpose of this study is to explore and compare research trends in Korea and overseas academic papers on social media marketing, and to present new academic perspectives for the future direction in Korea. Research design, data and methodology: We used English abstract of research paper (Korea's: 1,349, overseas': 5,036) for word frequency analysis, topic modeling, and trend analysis for each topic. Results: The results of word frequency and co-occurrence frequency analysis showed that Korea researches focused on the experiential values of users, and overseas researches focused on platforms and content. Next, 13 topics and 12 topics for Korea and overseas researches were derived from topic modeling. And, trend analysis showed that Korean studies were different from overseas in applying marketing methods to specific industries and they were interested in the short-term performance of social media marketing. Conclusions: We found that the long-term strategies of social media marketing and academic interest in the overall industry will necessary in the future researches. Also, data mining techniques will necessary to generate more general results by quantifying various phenomena in reality. Finally, we expected that continuous and various academic approaches for volatile social media is effective to derive practical implications.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.817-837
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• 2019

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

• 한국IT서비스학회지
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• 제19권6호
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권8호
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• pp.522-528
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• 2008

토픽맵에서 사용자의 토픽 선택에 따라 제공되는 정보는 개별 사용자의 관심과 배경지식이 고려되지 않고 최초 도메인 전문가에 의해 구축된 토픽맵 상의 토픽(Topic)과 연관되는 관계(Association), 자원(Occurrence)만을 이용하여 사용자에게 토픽맵 정보를 제공하고 있다. 이에 토픽맵은 개인화된 정보제공 측면의 단점을 보완하고자 개별 사용자를 위한 개인화 기능으로 개인 선호항목 설정, 필터링(Filtering), 범위제한(Scope) 등 사용자가 직접 관심정보를 사전에 설정하는 기능을 제공하고 있으나 토픽맵 사용자를 위한 개인화 측면에서 만족스럽지 못하다. 따라서 본 논문에서는 특정 도메인 토픽맵에서 사용자가 원하는 개인화된 정보를 제공하기 위해 사용자 클릭정보 수집을 통한 프로파일 정보와 이를 이용한 토픽 선호도 백터(Topic Preference Vector), 토픽맵 지식층의 기본요소인 토픽(Topic)과 관계(Association)를 이용한 개인화된 토픽맵 랭킹 알고리즘(PTR)을 제안한다. 사용자는 PTR 알고리즘을 이용하여 개인 선호도가 고려되어 랭킹된 토픽맵 정보를 제공받을 수 있게 됨으로써 개인화된 정보 제공 측면에서의 성능 향상을 가져올 수 있는 장점을 가진다.

• 한국프랜차이즈경영연구
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• 제12권4호
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• pp.13-23
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• 2021

Purpose: This study aimed to derive clues for the franchise industry to overcome difficulties such as various legal regulations and social responsibility demands and to continuously develop by analyzing the research trends related to franchises published in Korea. Research design, data and methodology: As a result of searching for 'franchise' in ScienceON, abstracts were collected from papers published in domestic academic journals from 1994 to June 2021. Keywords were extracted from the abstracts of 1,110 valid papers, and after preprocessing, keyword analysis, TF-IDF analysis, and topic modeling using LDA algorithm, along with trend analysis of the top 20 words in TF-IDF by year group was carried out using the R-package. Results: As a result of keyword analysis, it was found that businesses and brands were the subjects of research related to franchises, and interest in service and satisfaction was considerable, and food and coffee were prominently studied as industries. As a result of TF-IDF calculation, it was found that brand, satisfaction, franchisor, and coffee were ranked at the top. As a result of LDA-based topic modeling, a total of 12 topics including "growth strategy" were derived and visualized with LDAvis. On the other hand, the areas of Topic 1 (growth strategy) and Topic 9 (organizational culture), Topic 4 (consumption experience) and Topic 6 (contribution and loyalty), Topic 7 (brand image) and Topic 10 (commercial area) overlap significantly. Finally, the trend analysis results for the top 20 keywords with high TF-IDF showed that 10 keywords such as quality, brand, food, and trust would be more utilized overall. Conclusions: Through the results of this study, the direction of interest in the franchise industry was confirmed, and it was found that it was necessary to find a clue for continuous growth through research in more diverse fields. And it was also considered an important finding to suggest a technique that can supplement the problems of topic trend analysis. Therefore, the results of this study show that researchers will gain significant insights from the perspectives related to the selection of research topics, and practitioners from the perspectives related to future franchise changes.

• 한국정보시스템학회지:정보시스템연구
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• 제30권1호
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• pp.45-64
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• 2021

Purpose The traditional topic modeling technique makes it difficult to distinguish the semantic of topics because the key words assigned to each topic would be also assigned to other topics. This problem could become severe when the number of online reviews are small. In this paper, the extended model of topic modeling technique that can be used for analyzing a small amount of online reviews is proposed. Design/methodology/approach The extended model of being proposed in this paper is a form that combines the traditional topic modeling technique and the Word2Vec technique. The extended model only allocates main words to the extracted topics, but also generates discriminatory words between topics. In particular, Word2vec technique is applied in the process of extracting related words semantically for each discriminatory word. In the extended model, main words and discriminatory words with similar words semantically are used in the process of semantic classification and naming of extracted topics, so that the semantic classification and naming of topics can be more clearly performed. For case study, online reviews related with Udo in Tripadvisor web site were analyzed by applying the traditional topic modeling and the proposed extension model. In the process of semantic classification and naming of the extracted topics, the traditional topic modeling technique and the extended model were compared. Findings Since the extended model is a concept that utilizes additional information in the existing topic modeling information, it can be confirmed that it is more effective than the existing topic modeling in semantic division between topics and the process of assigning topic names.

• 무역학회지
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• 제48권1호
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• pp.55-81
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• 2023

본 연구의 목적은 한국 무역의 주요 이슈와 동향을 분석하고 향후 무역규범 연구에 대한 시사점을 도출하는데 있다. 분석자료로서 Korean Journal Citation Index 데이터베이스에서 2000년부터 2022년 7월까지 'Trade Rules'로 검색된 영문 키워드로 총 476개의 학술지를 분석하였다. 분석 방법으로는 동시발생네트워크와 텍스트마이닝 방법의 하나인 토픽트렌드 분석이 있다. 분석 결과, 최근 한국 무역을 대표하는 키워드는 연구 저널 수가 급증한 카테고리인 Topic 4(투자조약), Topic 7(무역안보), Topic 8(중국 보호무역주의), Topic 11(무역결제) 4가지로 나타났다. 이들 주제의 주요 배경은 기존의 국제무역 체제를 위협하는 미국과 중국 간의 무역마찰이며, 중국의 보호주의, 무역 안보 시스템의 변화, 새로운 투자 협정, 지불 방법의 변화에 대한 상세한 연구는 가까운 장래에 도전 과제가 될 것이다.