• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.3
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• pp.101-118
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• 2014

Given the context of rapid developing ubiquitous computing environment, it is imperative for users to search and use images based on affective meanings. However, it has been difficult to index affective meanings of image since emotions of image are substantially subjective and highly abstract. In addition, utilizing low level features of image for indexing affective meanings of image has been limited for high level concepts of image. To facilitate the access points of affective meanings of image, this study aims to utilize user-provided responses of images. For a data set, emotional words are collected and cleaned from twenty participants with a set of fifteen images, three images for each of basic emotions, love, sad, fear, anger, and happy. A total of 399 unique emotion words are revealed and 1,093 times appeared in this data set. Through co-word analysis and network analysis of emotional words from users' responses, this study demonstrates expanded word sets for five basic emotions. The expanded word sets are characterized with adjective expression and action/behavior expression.

• Journal of the Korean Society for information Management
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• v.24 no.4
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• pp.285-303
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• 2007

This study aims to explore a new technique making complementary linkage between controlled vocabularies and uncontrolled vocabularies for analyzing a research domain. Co-word analysis can be largely divided into two based on the types of vocabulary used: controlled and uncontrolled. In the case of using controlled vocabulary, data sparseness and indexer effect are inherent drawbacks. On the other case, word selection by the author's perspective and word ambiguity. To complement each other, we suggest a descriptor profiling that represents descriptors(controlled vocabulary) as the co-occurrence with words from the text(uncontrolled vocabulary). Applying the profiling to the domain of information science implies that this method can complement each other by reducing the inherent shortcoming of the controlled and uncontrolled vocabulary.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.1
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• pp.207-228
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• 2013

Due to the advance of scholarly communication, the field of open access has been studied over the last decade. The purpose of this study is to analyze and demonstrate the field of open access via co-word analysis. The data set was collected from Web of Science citation database during the period from January 1998 to July 2012 using the Topic category. A total of 479 journal articles were retrieved and 8,643 noun keywords were extracted from the titles and abstracts. In order to achieve the purpose of this study, network analysis, clustering analysis and multidimensional scaling mapping were used to examine the domain and the sub-domains of open access field. 18 clusters in the network analysis are recognized and 4 clusters are shown in the map of multidimensional scaling. In addition, the centrality analysis in the weighted networks was used to explore the significant keywords in this field. The results of this study are expected to demonstrate and guide the intellectual structure and new approaches of open access field.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.101-126
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• 2017

Data Science is emerging as a closely related field of study to Library and Information Science (LIS), and as an interdisciplinary subject combining LIS, statistics and computer science in an attempt to understand the value of data by applying what LIS has been doing for collecting, storing, organizing, analyzing, and utilizing information. To investigate which subject fields other than LIS, statistics, and computer science are related to Data Science, this study retrieved 667 materials from Web of Science Core Collection, extracted terms representing Web of Science Categories, examined subject fields that are studying Data Science using descriptive analysis, analyzed the intellectual structure of the field by co-word analysis and network analysis, and visualized the results as a Pathfinder network with clustering created with the PNNC clustering algorithm. The result of this study might help to understand the intellectual structure of the Data Science field, and may be helpful to give an idea for developing relatively new curriculum.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.9-15
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• 2019

Artiodactyla, which is an even-toed mammal, widely inhabits worldwide. In recent years, wild Artiodactyla species have attracted public attention due to the rapid increase of crop damage and road-kill caused by wild Artiodactyla such as water deer and wild boar and the decrease of some species such as long-tailed goral and musk deer. In spite of such public attention, however, there have been few studies on Artiodactyla in Korea, and no studies have focused on the trend analysis of Artiodactyla, making it difficult to understand actual problems. Many recent studies on trend used text-mining and co-occurrence analysis to increase objectivity in the classification of research subjects by extracting keywords appearing in literature and quantifying relevance between words. In this study, we analyzed texts from research articles of three countries (Korea, China, and Japan) through text-mining and co-occurrence analysis and compared the research subjects in each country. We extracted 199 words from 665 articles related to Artiodactyla of three countries through text-mining. Three word-clusters were formed as a result of co-occurrence analysis on extracted words. We determined that cluster1 was related to "habitat condition and ecology", cluster2 was related to "disease" and cluster3 was related to "conservation genetics and molecular ecology". The results of comparing the rates of occurrence of each word clusters in each country showed that they were relatively even in China and Japan whereas Korea had a prevailing rate (69%) of cluster2 related to "disease". In the regression analysis on the number of words per year in each cluster, the number of words in both China and Japan increased evenly by year in each cluster while the rate of increase of cluster2 was five times more than the other clusters in Korea. The results indicate that Korean researches on Artiodactyla tended to focus on diseases more than those in China and Japan, and few researchers considered other subjects including habitat characteristics, behavior and molecular ecology. In order to control the damage caused by Artiodactyla and to establish a reasonable policy for the protection of endangered species, it is necessary to accumulate basic ecological data by conducting researches on wild Artiodactyla more.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.11-14
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• 2013

본 연구의 목적은 바이오인포매틱스 분야의 회색문헌과 백색문헌의 초록을 대상으로 단어 동시출현(word co-occurrence)네트워크 분석을 통해 해당 분야의 연구 동향을 비교 분석하고자 하였다. 이를 위해 2010년부터 2012년까지 발표된 회색문헌인 회의자료(proceeding)와 백색문헌인 학술논문(journal article)의 초록을 SCOPUS, IEEEXplore, Microsoft academic search에서 수집하였다. 단어 동시출현 네트워크를 분석한 결과 회색문헌의 주요 연구는 분석도구 및 방법으로, 백색문헌의 주요 연구는 바이오인포매틱스의 주요 연구대상인 유전자 발현, 단백질 서열 및 구조 등으로 나타났다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.295-318
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• 2014

The purpose of this study is to analyze the intellectual structure of reading studies by using Co-Word Analysis based on the mixed weight in which the level of academic journals and the position of keywords are calculated. To achieve it, 838 academic articles relating to reading studies from KCI during the period from 2003 to 2012 were retrieved and 56 keywords were extracted. The results of clustering analysis, MDS, network analysis are that the network based on the mixed weight has a better performance in above three methods and reading studies can be divided into 4 bigger divisions and 11 subdivisions. Finally, the result of document analysis shows reading studies changes its research tendency from theoretical studies to empirical studies.

• Journal of the Korean Society for information Management
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• v.31 no.3
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• pp.293-311
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• 2014

This study explored the research trends on Twitter in Korea by informetric methods. All 539 articles on Twitter published from 2009 to the April of 2014 were obtained from the KCI. Only article titles, abstracts, and keywords by authors were used in analysis. Academic journals in many different disciplines where Twitter articles were produced were analysed by profiling, and then, the subject areas of researches on Twitter were analysed by co-word analysis. The results of this study showed that Twitter-related papers were published in as many as 53 disciplines with journalism, business administration, and computer science to be core fields. It was also found that the core subject areas are political issues and business.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.15-20
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• 2017

The purpose of this study is to present the intellectual structure of Assistive Technology Journal using co-word analysis of keywords. The articles of Assistive Technology Journal were collected from Web of Science citation database. 255 articles during the period from 2003 to 2015 were selected for the analysis. And 1,359 author keywords were extracted from the articles. In order to analyze the intellectual structure of Assistive Technology Journal, clustering analysis was conducted and 5 clusters were determined. Next, 5 clusters are presented in the map of multidimensional scaling. The results of this study are expected to assist in exploring the future directions of the researches on assistive technology.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.101-126
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• 2016

This paper identifies core research topics and their relationships by deriving the research topic networks in the technology management field using co-word analysis. Contrary to the conventional approach in which undirected networks are constructed based on normalized co-occurrence frequency, this study analyzes directed networks of keywords by employing the confidence index of association rule mining for pairs of keywords. Author keywords included in 2,456 articles published in nine international journals of technology management in 2011~2014 are extracted and categorized into three types: THEME, METHOD, and FIELD. One-mode networks for each type of keywords are constructed to identify core research keywords and their interrelationships with each type. We then derive the two-mode networks composed of different two types of keywords, THEME-METHOD and THEME-FIELD, to explore which methods or fields are frequently employed or studied for each theme. The findings of this study are expected to be fruitfully referred for researchers in the field of technology management to grasp research trends and set the future research directions.