• Annual Conference on Human and Language Technology
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• 1994.11a
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• pp.363-368
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• 1994

본 논문은 한글 문헌의 자동 키워드 추출을 위한 새로운 접근 기법을 제시한다. 한글에서 나타나는 형식형태소는 어절내에서 일정한 결합규칙을 가지며 또한 명사구나 동사구에서 보여지는 것처럼 어절간의 연결에도 관계된다. 유한개의 형식형태소를 노드로 하여 구성된 형태소 네트???p은 어휘사전 및 문헌을 통해 링크를 생성하게 되며 형태소분석과정에서 이를 이용하면 명사 추출의 정확성을 높일 수 있고 사전 탐색을 최소화하여 미등록어 추정 및 분석 속도를 향상시킬 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.694-697
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• 2017

최근 인터넷 기사 중에는 부정확한 제목이나 자극적인 단어를 사용하는 경우가 많아 구독자에게 불편함을 준다. 본 논문에서는 이러한 기사들의 헤드라인을 삭제하고, 기사의 내용을 3문장으로 요약해 주어, 구독자가 원하는 기사를 효율적으로 파악할 수 있게 하는 시스템을 제안한다. 제안하는 본 시스템은 파이썬 언어의 KoNLPy 패키지를 사용하여 기사의 단어들을 형태소 단위로 분석하며, 추출된 키워드를 토대로 워드 클라우드를 생성한다. 사용자가 클라우드의 특정 단어를 선택하면, 해당 신문기사들의 본문을 분석하여 각 신문 기사만의 핵심적인 문장을 3문장으로 출력해 준다.

• 한국벤처창업학회:학술대회논문집
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• 2023.11a
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• pp.163-167
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• 2023

사회복지 분야 감정노동은 타 감정노동 분야보다 높은 감정투입이 요구되는 분야로서 사회복지 분야 감정노동 관련 연구는 코로나 19 확산기점으로 연구가 감소되는 현상을 확인할 수가 있었는데 코로나 19의 엔데믹을 맞아 다시 복지현장에 투입되는 사회복지사들의 감정노동 현상을 지속적으로 파악, 연구하기 위해 감정노동 관련 연구는 지속적으로 증가해야 할 것으로 예상된다. 본 연구는 2010년부터 2023년까지 국내발표된 등재지, 등재후보지 논문을 대상으로 사회복지 분야 감정노동 연구동향을 파악하고자 한다. 연구동향 분석을 위해 총 119개의 논문을 대상으로 워드 클라우드, 동시출현빈도, 연도별 게재현황, 네크워트 분석을 실시할 예정이다.

• 대한공업교육학회지
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• v.40 no.2
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• pp.216-238
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• 2015

The purpose of this study was to identify the improvement plan of Korea Network for Occupations and Workers(KNOW) in terms of the information quality and usefulness for undergraduate students.'Homework of occupational information exploration'as research instrument was assigned to the engineering students taking career courses at KoreaTech in Chungnam province, Korea. Data was collected from two-hundred sixty six college students. The main results were as follows: The percentage of awareness of KNOW was very low(7.5%), but the mean of preference(73.5) and usefulness of career readiness(70.6) of KNOW were likely to be high. The information quality of KNOW was analysed in terms of both menu bar and evaluative components. In menu bar, the highest mean of information quality was'wage, job satisfaction, and job outlook'menu bar(71.7). On the other hand'task information on the job'had the lowest mean. In evaluative component, the highest mean of information quality was understandability(73.4), whereas the completeness was at the bottom(63.7). These results implied that the KNOW has the usefulness for engineering students to explore the occupational information and to make ready for career. Nevertheless, it is needed to publicize KNOW and to redeem menu bar and contents for the improvement of this online system.

• Knowledge Management Research
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• v.22 no.1
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• pp.217-242
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• 2021

The purpose of this study is to examine the research trends in the filed of individual knowledge hiding through keyword network analysis. As individuals intentionally hide their knowledge beyond not sharing their knowledge in organizations and the research on knowledge hiding steadily spreads, it is necessary to examine the research trends regarding knowledge hiding behaviors. For keyword network analyses, we collected 346 kinds of 578 keywords from 120 articles associated with knowledge hiding behaviors. We also transformed the keywords to 86 nodes and 667 links by data standardizing criteria and finally analyzed the keyword network among them. Moreover, this study scrutinized knowledge hiding trends by comparing the conceptual model for knowledge hiding based on literature review and the network structure based on keyword network analysis. As results, first, the network centrality degree, knowledge sharing, creativity, and performance was higher than others in Degree, Betweenness, Closeness centrality. Second, this study analyzed ego networks about psychological ownership and individual emotion theoretically associated with knowledge hiding and explored the relationship between variables through comparing with the conceptual model for knowledge hiding. Finally, the study suggested theoretical and practical implications and provided the limitations and suggestions for future research based on study findings.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.75-86
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• 2010

As the interests in health grow higher, necessity of Well-being relation informations get more importance. We get the information of well-being, tinternet retrieval system or blog, homepage and media. Although, it is not easy to find informations of well-being food. So, retrieval system has been requiring information about well-being food. In this paper, Weight-based Wellbeing Food Retrieval System is designed and implemention. Finding numerous pages and if well-being keywords includes page, it was identified and add weight. User searching for keywords, it implement, well-being food pages comes at the first. Keywords for discrimination makes type of dictionary, so it can insert, delete, modify. Inverted files saves hasing(direct-based file). Retrieval System in this paper is experimental result, at keywords of well-being food show 5~15% imprement than another Retrieval System. In this paper, Weight-based Wellbeing Food Retrieval System's designed and proposed way to raking for well-being food.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.879-886
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• 2018

The basic search engine on Twitter shows not only tweets that contain search keywords, but also all articles written by users with nicknames containing search keywords. Since the tweets unrelated to the search keyword are exposed as search results, it is inconvenient to many users who want to search only tweets that include the keyword. To solve this inconvenience, this study improved the search function of Twitter by developing an algorithm that searches only tweets that contain search keywords. The improved functionality is implemented as a Web service using ASP.NET MVC5 and is available to many users. We used a powerful collection method in C# to retrieve the results of an object, and it was also possible to output them according to the number of 'retweets' or 'favorites'. If the number of retrieved numbers is less than a given number, we also added an exclusion filter function. Thus, sorting search results by the number of retweets or favorites, user can quickly search for opinions that are of interest to many users. It is expected that many users and data analysts will find the developed function convenient to search on Twitter.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.209-216
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• 2023

In this paper, artificial intelligence agents ChatGPT and Stable Diffusion were used to explore the possibility of educational use by implementing a program to recommend picture materials for teaching and learning according to the class topic entered by teachers. The average time spent recommending all picture materials is about 6 minutes. In general, pictures related to keywords were recommended, and the letters in the recommended pictures could only know the intention to represent the letters, and the letters could not be recognized and the meaning could not be known. However, further research seems to be needed on the fact that the type or content of the recommended picture depends entirely on the response of ChatGPT and that it is not possible to accurately recommend the picture for all keywords. In addition, it was concluded that it is true that the recommended picture is related to the keyword, but the evaluation of whether it has educational value is the subject of discussion that should be left to the judgment of human teachers.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.113-128
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• 2021

In this study, we aimed to understand the public opinion on COVID-19 vaccine. To achieve the goal, we analyzed COVID-19 vaccine-related Twitter posts. 45,413 tweets posted from March 16, 2020 to March 15, 2021 including COVID-19 vaccine names as keywords were collected. The 12 vaccine names used for data collection included 'Pfizer', 'AstraZeneca', 'Modena', 'Jansen', 'NovaVax', 'Sinopharm', 'SinoVac', 'Sputnik V', 'Bharat', 'KhanSino', 'Chumakov', and 'VECTOR' in the order of the number of collected posts. The collected posts were analyzed manually and automatedly through keyword analysis, sentiment analysis, and topic modeling to understand the opinions for the investigated vaccines. According to the results, there were generally more negative posts about vaccines than positive posts. Anxiety about the aftereffects of vaccination and distrust in the efficacy of vaccines were identified as major negative factors for vaccines. On the contrary, the anticipation for the suppression of the spread of coronavirus following vaccination was identified as a positive social factor for vaccines. Different from previous studies that investigated opinions about COVID-19 vaccines through mass media data such as news articles, this study explores opinions of social media users using keyword analysis, sentiment analysis, and topic modeling. In addition, the results of this study can be used by governmental institutions for making policies to promote vaccination reflecting the social atmosphere.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.31-33
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• 2001

기존의 전자 상거래 시스템은 상품 정보를 순차적 혹은 계층적으로 나열하고 키워드 검색이나 계층적 탐색을 통해 상품을 선택하는 단순 방식을 사용하기 때문에 해당 도메인에 대한 전문적인 지식이 없는 일반 사용자들이 물품을 검색하기 어렵다. 이러한 문제점을 해결하기 위해 본 논문에서는 퍼지 기반 지능형 에이전트 시스템(Fuzzy Based Intelligent Agent System)을 제안한다. 퍼지 기반 지능형 에이전트 시스템은 사용자 중심의 지능형 상품 검색과 상품 선택 가이드에 전문가의 지식을 이용한다. 따라서, 상품 정보에 대한 전문적 지식이 없는 사용자를 지원하고, 사용자의 취향에 따라 동적으로 상품을 분류한 뷰를 제공할 수 있다.