• Digital Contents
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• no.6 s.109
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• pp.114-117
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• 2002

IT시장분석기관인 KRG(Knowledge Research Group)가 국내 300개 대학을 대상으로 조사해 발표한 '국내 대학 원격교육 도입 현황 보고서'에 따르면 국내 대학(2년제 포함)의 원격교육에 관한 관심이 날로 높아지고 있음을 알 수 있다. 네트워크 성능이 발달과 쌍방향 비실시간 교육에 대한 수요확대에 힘입어 국내 대학들은 절반 가까이가 원격교육 시스템을 도입하고 있으며, 비 도입 대학들도 60% 이상이 원격교육 시스템을 도입하거나 도입을 검토하고 있다. 특히 텍스트 중심의 교육보다는 동영상과 연계된 멀티미디어 원격교육에 대한 관심이 높아지고 있는 것으로 보인다. 국내 대학들의 원격교육 솔루션 도입 실태를 살펴보고, 앞으로 원격교육 시장의 발전을 예측해 보고자 한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.364-367
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• 2020

오디오 캡셔닝(Audio Captioning)은 시스템이 입력으로 오디오 신호를 받아들이고 해당 신호의 텍스트 설명을 출력하는 중간 번역 작업이다. 이 논문에서는 컨볼루셔널 뉴럴 네트워크(CNN), 트랜스포머의 딥러닝 알고리즘을 사용하여 주변 환경 소리에 대한 오디오 캡셔닝을 자동으로 수행하고 한글화된 출력 결과를 제공하는 모델을 제시한다. 본 연구 결과, 모델의 성능 평가 척도인 SPIDEr 점수는 0.1977이 나왔다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.167-176
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• 2021

With development of big data analysis technology, many studies to find social issues using texts mining techniques have been conducted. In order to derive social issues, previous studies performed in a way that collects a large amount of text data from news or SNS, and then analyzes issues based on text mining techniques such as topic modeling and terms network analysis. Social issues are the results of various social phenomena and factors. However, since previous studies focused on deriving social issues that are results of various causes, there are limitations to revealing the cause of the issues. In order to effectively respond to social issues, it is necessary not only to derive social issues, but also to be able to identify the causes of social issues. In this study, in order to overcome these limitations, we proposed a method of deriving the factors that cause social issues from texts related to social issues based on the theory of part of Korean linguistics. To do this, we collected news data related to social issues for three years from 2017 to 2019 and proposed a methodology to find causes based causal sentences based on text mining techniques.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.3
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• pp.211-219
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• 2001

텍스트 정보만을 이용하는 일반적인 검색엔진들의 한계를 극복하여 향상된 결과를 내기 위하여 링크 구조를 이용해 검색을 수행하는 시스템이 새롭게 등장하고 있다. 링크 구조는 사용자의 질의에 대해 중요한 문서들을 가려준다. 본 논문에서는 한 걸음 더 나아가 링크 정보를 이용하여 검색된 웹 페이지들 중 사용자의 기호에 적절한 결과를 도출하는 방법을 제안한다. 사용자 프로파일에 기반한 퍼지 개념 네트워크로 구축된 퍼지 문서 추출 시스템은 사용자의 성향을 반영하여 링크 기반 검색결과를 개인화 한다. 5명의 사용자에 대한 실험결과, 개발한 시스템이 의미 있는 웹 페이지를 검색함은 물론이고 사용자의 성향을 잘 반영함을 알 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.589-591
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• 2002

MFEG-4 컨텐츠는 사용자 상호작용의 지원과 바이너리 포맷의 지원으로 분산 환경에서 다중 사용자가 공유하기에 적합하다. 기존의 가상 환경을 생성하는 장면 기술 언어들은 텍스트 기반 기술 언어이의로 네트워크를 기반으로 하는 공유 환경에 적합하지 않다. MPEG-4의 기술 언어인 BIFS는 바이너리 포맷이므로, 생성된 컨텐츠의 용량이 작아서 네트워크 기반의 공유에 적합하며, 2차인 및 3차원의 풍부한 컨턴츠 생성을 가능하게 한다. 본 논문에서는 MPEG-4의 BIFS를 이용하여 가상 환경을 생성하고, 생성된 컨텐츠론 다중 사용자가 공유하여 가상환경에 참여할 수 있도록 하는 재생기폰 제안한다. 본 논문에서는 MIEG-4 컨텐츠 공유를 위하여 필요한 부분을 분석하여 기술하고, 분석된 기능을 토대로 MFEG-4 재생기를 설계하였다. MFEG-4 컨텐츠 공유를 위하여 본 재생기는 컨텐츠 공유 세션의 설정을 지원하고, 컨텐츠에 입력되는 사용자 이벤트를 공유 세설에 참여하는 모든 사용자에게 전송한다. 또한 전송된 사용자 이벤트를 입력받은 원격지 재생기는 공유 메시지를 해석하여 MFE7-4 씬을 갱신한다. 이러한 MPEG-4기반 공유 컨텐츠는 다중 사용자웅 게임 컨텐츠와 교육용 컨텐츠, 방송용 컨텐츠 등에 활용 될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.493-495
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• 2002

본 논문에서는 MPEG4 표준 코덱을 사용하여 다자간 멀티미디어 프로그램을 구현하는데 있어서 효율적으로 멀티미디어 데이터를 전송하는 방법과 그 구현에 대해서 연구한다. 다자간 멀티미디어 프로그램은 크게 음성, 영상, 텍스트의 멀티미디어 요소를 가지고 있으며 각각의 데이터는 네트워크를 통해 여러 사용자에게 전송이 되는데 네트워크를 통해 전송되어지는 데이터를 최소화하기 위한 여러가지 방법을 제시하고 각 방법에 대한 전송효율에 대하여 분석한다. 본 논문에서는 서버-클라이언트 모델을 사용하여 이 방법들을 구현하였으며 현재 8명의 사용자가 동시에 접속하여 다자간 멀티미디어 프로그램을 사용할 수 있도록 하였고 그 이상의 사용자 수에 대해서도 변경할 수 있도록 하였다.

• Journal of Korean Library and Information Science Society
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• v.32 no.2
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• pp.349-363
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• 2001

The study suggests information processing model and discusses variety of information due to change and expansion of text, comparies information service in cyberspace and reality and presents reference interview in on - line service.