• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.431-453
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• 2022

Hate is a collective expression of exclusivity toward others and it is fostered and reproduced through false public perception. This study aims to explore the objects and issues of hate discussed in our society using text mining techniques. To this end, we collected 17,867 news data published from 1990 to 2020 and constructed a co-word network and cluster analysis. In order to derive an explicit co-word network highly related to hate, we carried out sentence split and extracted a total of 52,520 sentences containing the words 'hate', 'prejudice' and 'discrimination' in the preprocessing phase. As a result of analyzing the frequency of words in the collected news data, the subjects that appeared most frequently in relation to hate in our society were women, race, and sexual minorities, and the related issues were related laws and crimes. As a result of cluster analysis based on the co-word network, we found a total of six hate-related clusters. The largest cluster was 'genderphobic', accounting for 41.4% of the total, followed by 'sexual minority hatred' at 28.7%, 'racial hatred' at 15.1%, 'selective hatred' at 8.5%, 'political hatred' accounted for 5.7% and 'environmental hatred' accounted for 0.3%. In the discussion, we comprehensively extracted all specific hate target names from the collected news data, which were not specifically revealed as a result of the cluster analysis.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.79-84
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• 2008

많은 사용자들의 참여로 구축된 집단 지성을 이용한 지식 검색 서비스에서 사용자가 원하는 답변을 빨리 찾고자 하는 요구가 증가하고 있다. 기존의 연구에서 조회 수, 추천 수, 답변 수와 같은 비텍스트 정보가 답변을 평가하는데 좋은 자질임이 증명되었고, 신뢰도를 추정할 수 있는 여러 종류의 단어 사전을 이용하여 답변의 좋고 나쁨을 평가할 수 있는 연구도 진행되었다. 하지만, 조회 수, 추천 수, 답변 수와 같은 비텍스트 정보는 사용자 조작이 간단하여 지속적으로 관리를 해야 하며, 신뢰도를 추정할 수 있는 단어는 지속적으로 보강되어야 한다. 본 논문에서는 이러한 문제점을 해결하고자 동시출현 자질을 이용한 질문과 답변의 유사성을 활용하여 집단 지성에서 사용자의 활동을 분석하여 사용자의 명성을 평가하는 방법을 제안한다. 사용자의 명성을 계산할 수 있다면 조회 수와 추천 수가 많지 않은 답변의 신뢰도도 비교적 정확하게 추정할 수 있다. 이를 위해 우리는 PageRank 알고리즘을 수정하여 사용자 명성을 계산한다. 네이버 지식iN의 문서로 실험한 결과, 기존 정답 선택률을 보완할 수 있는 결과를 보였다.

• 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.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.459-476
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• 2008

The user needs to find the answer to your question is growing fast at the service using collective intelligent knowledge. In the previous researches, it was proven that the non-text information like view counting, referrer number, and number of answer is good in evaluating answers. There were also many works about evaluating answers using the various kinds of word dictionaries. In this work, we propose new method to evaluate answers to question effectively using user reputation that estimated by the social activity. We use a modified PageRank algorithm for estimating user reputation. We also use the similarity between question and answer. From the result of experiment in the Naver GisikiN corpus, we can see that the proposed method gives meaningful performance to complement the answer selection rate.

• Journal of Information Management
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• v.41 no.2
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• pp.1-30
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• 2010

This study analyzed research output in dominant research areas of 19 national research institutions. Policy knowledge produced by the institutions during the past 5 years mainly concerned 10 policies dealing with economy and society issues. Similarities between the research subjects of the institutions were displayed by MDS mapping. The study also identified issue attention cycles of the 5 chosen policies and examined the correlation between the issue attention cycles and the yields of policy knowledge. The knowledge structure of each policy was mapped using co-word analysis and Ward's clustering. It was also found that the institutions performing research on similar subjects demonstrated citation preferences for each other.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.263-287
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• 2021

Researches on inequality in Korean society has been sporadically conducted in various areas. In this study, research trend related to inequality was analyzed through basic statistical analysis, co-occurrence analysis, and main path analysis using articles related to inequality from Korea citation index. In basic statistical analysis, key authors, journals, and articles are identified. In co-occurrence analysis, income inequality, educational inequality, welfare inequality, and policy on inequality were identified as main topics. Main path analysis showed two research trends after 2004. One was research trend on economic inequality, and the other was on health inequality and social structural inequality.

• Explosives and Blasting
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• v.40 no.1
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• pp.1-16
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• 2022

In order to develop effective and safe blasting techniques or to introduce foreign advanced blasting techniques to domestic industry, the analysis of research trend in blasting field in the world is essential. In generally, such a research trend analysis was carried out for limited number of published papers. In this study, a bibliometric analysis was performed using VOSviewer for the overall papers published in international journals to figure out the variation of research trend in blasting area. From the keyword analysis, it was found that the number of published papers and the number of overall keywords was limited in the 2000s. Since 2010, the number of published papers was increased rapidly and the keywords were diversified with the introduction of artificial intelligence(AI). The keyword analysis for 2017~2021 showed that various hybrid AI techniques were actively applied in the evaluation of blasting effect.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.411-438
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• 2024

This study aims to identify and comprehensively view health information-related research trends using a bibliometric analysis. To this end, 1,193 papers from 2002 to 2023 related to "health information" were collected through the Korea Citation Index (KCI) database and analyzed in diverse aspects: research trends by period, academic fields, intellectual structure, and keyword changes. Results indicated that the number of papers related to health information continued to increase and has been decreasing since 2021. The main academic fields of health information-related research included "biomedical engineering," "preventive medicine/occupational environmental medicine," "law," "nursing," "library and information science," and "interdisciplinary research." Moreover, a co-word analysis was performed to understand the intellectual structure of research related to health information. As a result of applying the parallel nearest neighbor clustering (PNNC) algorithm to identify the structure and cluster of the derived network, four clusters and 17 subgroups belonging to them could be identified, centering on two conglomerates: "medical engineering perspective on health information" and "social science perspective on health information." An inflection point analysis was attempted to track the timing of change in the academic field and keywords, and common changes were observed between 2010 and 2011. Finally, a strategy diagram was derived through the average publication year and word frequency, and high-frequency keywords were presented by dividing them into "promising," "growth," and "mature." Unlike previous studies that mainly focused on content analysis, this study is meaningful in that it viewed the research area related to health information from an integrated perspective using various bibliometric methods.