• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.2
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• pp.235-248
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• 2011

In this study, co-word occurrence analysis was performed on 288 articles rerieved from the Web of Science DB with the topic of web archiving. Results showed that research on image archiving information technology and system were most frequently carried out especially in medical area. Within library and information science and records management & archives areas, web archiving/digital preservation project subject and web archiving tools and methodology subject were studied mostly. It is expected that research related to web archiving software and tools will be increased in near future.

• Annual Conference on Human and Language Technology
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• 1999.10e
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• pp.215-220
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• 1999

본 연구에서는 정해진 주제내에서 높은 번역율과 빠른 처리 시간을 동시에 수용할 수 있는 효과적인 다국어 채팅 시스템을 구현하기 위해 사용자가 어절 단위로 단어를 입력하거나 선택하여 이미 구축되어 있는 문형에 접근하도록 유도하는 사용자 문형확장 방식을 제안하였다. 사용자 문형확장 방식을 사용하여 다국어 채팅 시스템을 구현할 경우 사용자 입력과 동시에 구문분석, 변환, 생성등 일련의 번역과정을 최소한의 처리시간으로 처리할 수 있으므로 매우 용이하게 실시간 번역 시스템을 구현할 수 있다는 장점이 있다. 사용자 문형확장 방식과 더불어 이와 함께 사용될 수 있는 통합 문형코드와 통합 품사체계도 제안하였다. 이는 번역의 대상이 되는 한국어, 영어, 일본어 각각에 대해 문형코드와 품사코드를 따로 설정하지 않고 통일된 하나의 코드체계를 적용함으로써 기계번역에서의 변환과정을 최소화하기 위해 고안하였다.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.83-96
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• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• 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 the Korean BIBLIA Society for library and Information Science
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• v.29 no.2
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• pp.85-108
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• 2018

This study aimed to analyze media reports of the Comprehensive Library Advancement Plan using big data analysis in order to determine trends and implications by period. To do so, related data from 2009 to 2017 were collected from major domestic web portal sites. Words in the collected data were refined through the text mining process and frequency, centrality, and structural equivalence analyses were performed. Results confirmed that, during the implementation of the first and the second phases of the Comprehensive Library Advancement Plan, the focus of the library policy changed from external growth to strengthening internal stability and advancement of library operation, and the media coverage were limited to specific policies such as expansion of library facilities. Findings from this study will serve as useful material for ascertaining the approach to perceive and understand the national library policy represented by the Comprehensive Library Advancement Plan.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.24-24
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• 2010

중등 지구과학교과서와 대학 교재에서 학습하는 광물과 암석 이름은 대부분 영어, 한자, 일본어에서 도입한 용어이다. 이 용어에 대한 어원과 말뜻에 대한 분석이나 연구가 되지 않은 상태에서 바로 사용해왔기 때문에 지질학 초보자들이 학습하기에 아주 어렵다. 광물과 암석이름의 어원과 말뜻을 잘 알지 못하고 단순히 외우거나 학술적인 이름이나 의미만을 생각하고 사용하고 있으며, 한 광물이나 암석에 대하여 여러 가지 이름을 사용하기도 한다. 심지어 전혀 엉뚱한 암석 이름이 대중 사이에서 사용되고 있지만 이를 통제하지도 못하고 그 명칭이 틀렸다는 것도 모르고 있다. 예를 들면 영어로 Shale을 중등 교과서와 대학 교재에서 영어 발음을 따라 한국어로 셰일이라고 표기하지만 중국과 일본에서는 혈암(頁岩)으로 표기한다. 우리나라의 대중 매체의 인터넷 사전과 대중들이 사용하는 용어는 중국어 혈암(頁岩)을 공공연하게 '혈암'으로 표기하고 있다. '혈(頁)'을 한자 사전에서 찾아보면 '머리 혈'과 '책 면 엽'으로 정리되어 있다. 그러면 셰일의 암석학적 특징으로 볼 때 혈암이라고 해야 하나? 엽암이라고 해야 하나? 과학과의 다른 분야에서는 어려운 한자를 쉬운 한글로 표준화하는 연구와 실행을 꾸준히 진행해오고 있다. 생물의 경우 생물의 어려운 학명을 이미 쉬운 한글로 표준화했으며, 그 학명이 학생과 대중들에게 널리 알려져 있다. 지구과학의 교과서 문장에 나오는 단어와 전문용어가 한자를 한글로 표기한 경우가 많은데 이 단어들을 하루 속히 한글로 표준화하여 전문가들이 먼저 사용하는 동시에 학생과 대중들에게 알려 주어야 한다. 이렇게 되면 지구과학의 내용보다 용어가 어렵다는 인식을 바꾸어 줄 것이고 지구과학을 전공하고자 하는 학생들에게 희망과 용기를 줄 것이다. 그 일환으로 광물과 암석 이름의 어원을 조사해 보고 한글 표준화의 가능성을 타진해 보고자 한다.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.329-333
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• 2022

상호참조해결은 주어진 문서에서 상호참조해결 대상이 되는 멘션(mention)을 식별하고, 동일한 개체(entity)를 의미하는 멘션들을 찾아 그룹화하는 자연어처리 태스크이다. 최근 상호참조해결에서는 BERT를 이용하여 단어의 문맥 표현을 얻은 후, 멘션 탐지와 상호참조해결을 동시에 진행하는 End-to-End 모델이 주로 연구가 되었다. 그러나 End-to-End 방식으로 모델을 수행하기 위해서는 모든 스팬을 잠재적인 멘션으로 간주해야 되기 때문에 많은 메모리가 필요하고 시간 복잡도가 상승하는 문제가 있다. 본 논문에서는 서브 토큰을 다시 단어 단위로 매핑하여 상호참조해결을 수행하는 워드 레벨 상호참조해결 모델을 한국어에 적용하며, 한국어 상호참조해결의 특징을 반영하기 위해 워드 레벨 상호참조해결 모델의 토큰 표현에 개체명 자질과 의존 구문 분석 자질을 추가하였다. 실험 결과, ETRI 질의응답 도메인 평가 셋에서 F1 69.55%로, 기존 End-to-End 방식의 상호참조해결 모델 대비 0.54% 성능 향상을 보이면서 메모리 사용량은 2.4배 좋아졌고, 속도는 1.82배 빨라졌다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.2
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• pp.189-209
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• 2019

Main Path Analysis (MPA) is one of the text mining techniques that extracts the core literature that contributes knowledge transfer based on citation information in the literature. This study applied various text mining techniques to abstract of the paper related with sense-of-place, which is published at Korea and abroad from 1990 to 2018 so that could discuss in a macro perspective. The main path analysis results showed that from 1990, overseas research on sense-of-place has been carried out in the order of personal identity, public land management, environmental education and urban development-related areas. Also, by using the network analysis, this study found that sense-of-place was discussed at various levels in Korea, including urban development, culture, literature, and history. On the other hand, it has been found that there are few topic changes in international studies, and that discussions on health, identity, landscape and urban development have been going on steadily since the 1990s. This study has implications that it presents a new perspective of grasping the overall flow of relevant research.