• Journal of Information Processing Systems
• /
• v.16 no.2
• /
• pp.447-459
• /
• 2020

After reading a document, people construct a concept about the information they consumed and merge multiple words to set up keywords that represent the material. With that in mind, this study suggests a smarter and more efficient keyword extraction method wherein scholarly journals are used as the basis for the establishment of production rules based on a concept information of words appearing in a document in a way in which author-provided keywords are functional although they do not appear in the body of the document. This study presents a new way to determine the importance of each keyword, excluding non-relevant keywords. To identify the validity of extracted keywords, titles and abstracts of journals about natural language and auditory language were collected for analysis. The comparison of author-provided keywords with the keyword results of the developed system showed that the developed system was highly useful, with an accuracy rate as good as up to 96%.

• Knowledge Management Research
• /
• v.18 no.3
• /
• pp.81-99
• /
• 2017

This study is to compare the main subjects of domestic and foreign knowledge management research in terms of keywords and to clarify whether domestic knowledge management research reflects research trends in overseas knowledge management research. Specifically, we try to find out whether the central activities such as knowledge sharing, knowledge generation, and acquisition, which are knowledge management activities of knowledge management research, are being studied without bias. In order to analyze this, we analyzed the data of domestic and foreign knowledge management research for the last 5 years from 2012 to 2016. In Korea, the Knowledge Management Society of Korea collected 167 papers and 787 keywords, and collected 132 papers and 640 keywords from the Korea Society of Management Information Systems in order to distinguish the research areas. Overseas papers collected 315 papers and 1,746 keywords published by Emerald. Also, we collected 382 papers and 1,633 keywords in the Korean Management Review and collected 646 papers and 2,879 keywords in the Korean Business Education Review. Frequency analysis and network analysis of 1,642 papers and 7,685 keywords are summarized as follows. The Knowledge Management Society of Korea has focused on knowledge sharing, and in 2016, interest in knowledge transfer and knowledge search has shifted. The Journal of Knowledge Management, which is published by Emerald, has been a major concern for knowledge transfer and knowledge sharing. The research trends of the Korea Society of Management Information Systems to distinguish a clear identity of knowledge management research are focusing on smart area and mobile domain such as information security domain, cloud, smart phone, and smart work. In the Korea Society of Management Information Systems research, the main subject of knowledge sharing is also commonly found.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.758-764
• /
• 2018

Digital documents such as patents, scholarly papers and research reports have author keywords which summarize the topics of documents. Different documents are likely to describe the same topic if they share the same keywords. Document clustering aims at clustering documents to similar topics with an unsupervised learning method. However, it is difficult to apply to a large amount of documents event though the document clustering is utilized to in various data analysis due to computational complexity. In this case, we can cluster and connect massive documents using keywords efficiently. Existing bottom-up hierarchical clustering requires huge computation and time complexity for clustering a large number of keywords. This paper proposes an inverted index based bottom-up clustering for keywords and analyzes the results of clustering with massive keywords extracted from scholarly papers and research reports.

• Journal of Korean Academy of Nursing
• /
• v.49 no.5
• /
• pp.538-549
• /
• 2019

Purpose: This study aimed to explore and compare the knowledge structure of pain management nursing research, between Korea and other countries, applying a text network analysis. Methods: 321 Korean and 6,685 international study abstracts of pain management, published from 2004 to 2017, were collected. Keywords and meaningful morphemes from the abstracts were analyzed and refined, and their co-occurrence matrix was generated. Two networks of 140 and 424 keywords, respectively, of domestic and international studies were analyzed using NetMiner 4.3 software for degree centrality, closeness centrality, betweenness centrality, and eigenvector community analysis. Results: In both Korean and international studies, the most important, core-keywords were "pain," "patient," "pain management," "registered nurses," "care," "cancer," "need," "analgesia," "assessment," and "surgery." While some keywords like "education," "knowledge," and "patient-controlled analgesia" found to be important in Korean studies; "treatment," "hospice palliative care," and "children" were critical keywords in international studies. Three common sub-topic groups found in Korean and international studies were "pain and accompanying symptoms," "target groups of pain management," and "RNs' performance of pain management." It is only in recent years (2016~17), that keywords such as "performance," "attitude," "depression," and "sleep" have become more important in Korean studies than, while keywords such as "assessment," "intervention," "analgesia," and "chronic pain" have become important in international studies. Conclusion: It is suggested that Korean pain-management researchers should expand their concerns to children and adolescents, the elderly, patients with chronic pain, patients in diverse healthcare settings, and patients' use of opioid analgesia. Moreover, researchers need to approach pain-management with a quality of life perspective rather than a mere focus on individual symptoms.

• Journal of Korean Clinical Nursing Research
• /
• v.25 no.1
• /
• pp.80-90
• /
• 2019

Purpose: The aim of this study was to identify the research trends of articles published in the Journal of Korean Clinical Nursing Research from 2000 to 2017 by a text network analysis using keywords. Methods: This study analyzed 600 articles. The R program was used for text mining that extracted frequency, centrality rank, and keyword network. Results: From 2000 to 2009, keywords with high-frequency were 'nurse', 'pain', 'anxiety', 'knowledge', 'attitude', and so on. 'Pain', 'nurse', and 'knowledge' showed a high centrality. 'Fatigue' showed no high frequency but a high centrality. Keywords such as 'nurse', 'knowledge', and 'pain' also showed high frequency and centrality between 2010 and 2017. 'Hemodialysis' and 'intensive care unit' were added to keywords with high frequency and centrality during the period. Conclusion: The frequency and centrality of keywords such as 'nurse', 'pain', 'knowledge', 'hemodialysis', and 'intensive care unit' reflect the research trends in clinical nursing between 2000 and 2017. Further studies need to expand the keyword networks by connecting the main keywords.

• The Journal of the Korea Contents Association
• /
• v.20 no.12
• /
• pp.23-35
• /
• 2020

This study aims to examine trends in the IoT field by collecting and analyzing domestic papers on IoT that will have a great impact across industries and society. The survey period for this study was from 2015 to 2019, and the domestic papers on the IoT were collected using Naver's Academic Information. We extracted the keywords with high frequency from the domestic papers collected by the period and performed the centrality analysis to identify the central keywords among the keywords with high frequency. In terms of keyword frequency, 'sensor' and 'security' from 2015 to 2017 appeared as the top keywords with high frequency. From 2017, 'car' and 'intelligence' appeared as the top keywords with high frequency. In terms of keyword centrality, 'security' and 'sensor' from 2015 to 2016 appeared as highly centralized keywords. From 2017, 'intelligence', 'car' and 'industrial revolution' appeared as highly centralized keywords.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.3
• /
• pp.85-90
• /
• 2022

In this study, keywords from representative online portal sites such as NAVER, Google, and Youtube were collected based on text mining analysis technique using TEXTOM to check the changes in the restaurant industry before and after COVID-19. The collection keywords were selected as dining out, food service industry, and dining out culture. For the collected data, the top 30 words were derived, respectively, through the refinement process. In addition, comparative analysis was conducted by defining data from 2018 to 2019 before COVID-19, and from 2020 to 2021 after COVID-19. As a result, 8272 keywords before COVID-19 and 9654 keywords after COVID-19, a total of 17926 keywords, were derived. In order for the food service industry to develop after the COVID-19 pandemic, it is necessary to commercialize the recipes of restaurants to revitalize the distribution of home-use food products that replace home-cooked meals such as meal kits. Due to the social distancing caused by COVID-19, the dining out culture has changed and the trend has changed, and it has been confirmed that the consumption culture has changed to eating and delivering at home more safely than visiting restaurants. In addition, it has been confirmed that the consumption culture of existing consumers is changing to a trend of cooking at home rather than visiting restaurants.

• The Korean Journal of Emergency Medical Services
• /
• v.16 no.2
• /
• pp.43-51
• /
• 2012

Purpose : We try to disclose how much the keywords of the papers from the Korean Journal of Emergency Medical Services with Medical Subject Headings(MeSH) terminologies and to understand the major subjects of the recent emergency medical technology research in Korea from keywords. Methods : We analyzed keywords from 524 articles of the Korean Journal of Emergency Medical Services that were published between 2005 and 2011. We investigated frequently used keywords and what percentages of keywords agree with MeSH terms using the MeSH browser. Results : There were on average 3.2 keywords per article. The most frequent key words were AED, Attitude, Cardiopulmonary Resuscitation, CPR, EMT, EMT students, External Defibrillator, Job satisfaction, Knowledge, 119 EMT in order. The number of terms in precise agreement with MeSH headings was 101(19.3%); 327 terms(62.4%) were not found in the MeSH browser and 96 terms(18.3%) partially matched MeSH terms. Conclusion : Many keywords used in the Korean Journal of Emergency Medical Services did not agree with MeSH terms. We conclude that contribution rules should be using MeSH terms and authors should be educated in the proper use of MeSH terms in their research and subsequent publication.

• Journal of the Korean Society of Safety
• /
• v.36 no.1
• /
• pp.56-63
• /
• 2021

The narrative texts of industrial accident reports help to identify accident risk factors. They relate the accident triggers to the sequence of events and the outcomes of an accident. Particularly, a set of related keywords in the context of the narrative can represent how the accident proceeded. Previous studies on text analytics for structuring accident reports have been limited to extracting individual keywords without context. We proposed a context-based analysis using a Natural Language Processing (NLP) algorithm to remedy this shortcoming. This study aims to apply Word2Vec of the NLP algorithm to extract adjacent keywords, known as word embedding, conducted by the neural network algorithm based on supervised learning. During processing, Word2Vec is conducted by adjacent keywords in narrative texts as inputs to achieve its supervised learning; keyword weights emerge as the vectors representing the degree of neighboring among keywords. Similar keyword weights mean that the keywords are closely arranged within sentences in the narrative text. Consequently, a set of keywords that have similar weights presents similar accidents. We extracted ten accident processes containing related keywords and used them to understand the risk factors determining how an accident proceeds. This information helps identify how a checklist for an accident report should be structured.

• Journal of Fisheries and Marine Sciences Education
• /
• v.28 no.3
• /
• pp.872-880
• /
• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.