• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.73-80
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• 2008

In this paper, an advertisement-keyword finding method using document clustering is proposed to solve problems by ambiguous words and incorrect identification of main keywords. News articles that have similar contents and the same advertisement-keywords are clustered to construct the contextual information of advertisement-keywords. In addition to news articles, the web page and summary of a product are also used to construct the contextual information. The given document is classified as one of the news article clusters, and then cluster-relevant advertisement-keywords are used to identify keywords in the document. We could achieve 21% precision improvement by our proposed method.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.5
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• pp.409-419
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• 2016

In general, Area studies have very flexible field of research, so it is very difficult to proceed all field of research at the same time. Due to this, researches on Area studies have been changed the field of research and research trends according to age. So it is important to identify research trends for performing Area studies. Also, interests for understanding the research trend of Area studies are increasing constantly. In this paper, we analyze research trends of Mediterranean Area studies in Korea by using co-appearance keywords. To do this, we first analyze article types and extract co-appearance keywords on articles of 『Journal of Mediterranean Area Studies』, which is the representative journal of Mediterranean region in Korea. In details, trends analysis of Mediterranean Area studies would be performed by using cp-keywords of article and visualizing network graph forms.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.3
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• pp.417-439
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• 2009

In this study, the intellectual structure of Records Management and Archival Science in Korea was analyzed based on the syntactic and semantic structure analysis of article titles. The data used in this study were 344 articles from three major representative journals in the field of Records Management and Archival Science, published from 1999 to 2008. The results of the syntactic and semantic structure analysis of article titles show that the three role concepts of keywords are 'research domain', 'research object', and 'research focus'. Keywords in article titles were clustered into the core subject areas after they were assigned three concepts. Based on the results of cluster analysis, the intellectual structure of Records Management and Archival Science in Korea was proposed.

• Journal of Fashion Business
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• v.23 no.3
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• pp.51-66
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• 2019

The aim of this study is to identify research trends of "Journal of Fashion Business" by analyzing the keyword network of the paper published between 2006 and 2017. The papers selected for analysis in the study were 287 fashion design articles and 281 fashion marketing articles published between February 2006 and December 2017 and titles, volumes, publishing years, authors, keywords, and abstracts of each paper were collected for data analysis. The research was carried out through selection, collection of article data, keyword extraction and coding, keywords refinement, formation of network matrix, and analysis and visualization process. First, based on the title of the paper used in the analysis, the fashion design/aesthetics, marketing/social psychology, clothing materials, clothing composition, and other fields were classified. Research analysis used the Netminer 4 (Ver.4.3.2) program. Results indicated showed that the intellectual structure of the "Fashion Business" research paper showed key word changes over time, and the degree centrality and between centrality of the keywords.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.999-1005
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• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.607-614
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• 2014

This article aims to explore and discuss research trends in global cruise industry using keyword network analysis. We visualize keyword networks in each of four groups of 1982-1999, 2000-2004, 2005-2009, 2010-2014 based on the top 20 keyword nodes' degree centrality and betweenness centrality which are selected among four centrality measurements, comparing them with frequency order. The article shows that keyword frequency collected from 240 articles published in international journals is subject to Zipf's law and nodes degree distribution also exhibits power law. We try to find out research trends in global cruise industry to change some important keywords diachronically, visualizing several networks focusing on the top two keywords, cruise and tourism, belonging to all the four year groups, with high degree and betweenness centrality values. Interestingly enough, a new node, China, connecting the top most keywords, appears in the most recent period of 2010-2014 when China has emerged as one of the rapid development countries in global cruise industry. Therefore keyword network analysis used in this article will be useful to understand research trends in global cruise industry because of increase and decrease of numbers of network types in different year groups and the visual connection between important nodes in giant components.

• Journal of Preventive Medicine and Public Health
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• v.41 no.6
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• pp.365-372
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• 2008

Objectives : There are very few researches on North Korea's academic activities. Furthermore, it is doubtful that the available data are reliable. This study investigated research activities and knowledge structure in the field of Preventive Medicine in North Korea with a network analysis using co-authors and keywords. Methods : The data was composed of the North Korean Journal of preventive medicine ranged from Vol. 1 of 1997 to Vol. 4 of 2006. It was the matrix of 1,172 articles by 1,567 co-authors. We applied R procedure for keywords abstraction, and then sought for the outcome of network forms by spring-KK and shrinking network. Results : To comprehend the whole networks explicitly demonstrated that the academic activities in North Korea s preventive medicine were predisposed to centralization as similar as South Korea's, but on the other aspect they were prone to one-off intermittent segmentation. The principal co-author networks were formulated around some outstanding medical universities seemingly in addition to possible intervention by major researchers. The knowledge structure of network was based on experimentation judging from keywords such as drug, immunity, virus detection, infection, bacteria, anti-inflammation, etc. Conclusions : Though North Korea is a socialist regime, there were network of academic activities, which were deemed the existence of inducive mechanism affordable for free research. Article keywords has laid greater emphasis on experiment-based bacterial defection, sustainable immune system and prevention of infection. The kind of trend was a consistent characteristic in preventive medicine of North Korea haying close correlation with Koryo medical science.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.285-302
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• 2024

For this article, we conducted frequency analysis, topic modeling, and network analysis on eleven texts related to collection development policy found in the National Library of Korea. We deduced the main keywords related to collection development policies and analyzed the relationship between them. We subsequently conducted a pie coefficient analysis to identify the characteristics of collection development policies of university libraries and public libraries by category. The results showed that keywords such as "material," "library," "collection development," "user," and "collection" were the main keywords in frequency analysis and network centrality. Meanwhile, the pie coefficient analysis revealed that keywords such as "university," "construction," "student," "target," and "cost" were prevalent in university libraries, indicating that the academic needs of users and the discussion of digital resources were primary issues, while keywords related to the information needs of various user groups-including "adults," "survey," "feature," and "religion" -appeared in public libraries.

• Child Health Nursing Research
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• v.21 no.4
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• pp.347-354
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• 2015

Purpose: This descriptive study was performed to explore trends in child health nursing research by analyzing the themes, contents and structure of articles published in 2014 in Child Health Nursing Research, the official journal of the Korean Academy of Child Health Nursing. Methods: Thirty-eight articles were reviewed using keywords, author (s), subjects, ethical considerations, designs, statistics involved, funding resources, and others. Results: Ten domains from 160 keywords were identified as follows, child related, psycho-social variable related, parents and family related, nursing and health related, and others. A mean of 2.9 authors per article was identified and 71% of the authors were academic- affiliated. Twenty-eight articles were human-participant related while 21 articles addressed both Institutional Review Board and written consent. Non-experimental design was the most commonly used method followed by experimental design, and qualitative design. The duration for acceptance was a mean of 89.1 days from submission with most articles requiring a second round of article review. Half of the articles were supported by grant organizations such as Korean National Research Foundation. Conclusion: The findings of the analysis show an improvement in the scientific quality with a diversity of articles in Child Health Nursing Research.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.731-738
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• 2013

We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.