• Journal of Korean Library and Information Science Society
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• v.48 no.1
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• pp.207-225
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• 2017

The aim of this study was to discover various Library and Information Science (LIS) research areas by examining similarities and differences between LIS journals in terms of keyword characteristics. To conduct this study, for the years from 2004 to 2016, the keywords of 6 international journals were downloaded from Scopus database (http://www.scopus.com), and the keywords of 4 Korean journals were downloaded from the RISS database (http://www.riss.co.kr). The characteristics of keywords were investigated by examining frequently used keywords and frequently used distinctive keywords pertaining to international and Korean journals. The distinctive keywords are referred to as the keywords that appear in one domain but not in another. The result of this study indicated the following: a) a frequency analysis of the keywords showed major research themes and unique traits concerning Korea. b) In general, the keywords used in Korean journals frequently reflected the library as a major subject area of research, while keywords used in international journals reflected bibliometrics and information retrieval as major subject areas of research. c) The overarching themes of each created dataset were clearly noticeable in frequently used distinctive keywords. d) Some keywords were bound by a nation or by a region due to their scope of usage. The important implication of this study is that both most frequently used keywords and most frequently used distinctive keywords seemed to adequately represent the LIS subject areas.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.255-263
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• 2023

Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear. Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations. Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was 'treatment,' followed by 'fear,' 'dental implant,' 'conscious sedation,' 'pain,' 'dental fear,' 'comfort,' 'taking medication,' 'experience,' and 'tooth.' In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that 'fear and treatment' and 'treatment and pain' appeared the most frequently. Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.

• Journal of Korean Academy of Nursing Administration
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• v.13 no.1
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• pp.118-122
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• 2007

Purpose: This study was to understand the major subjects of the recent nursing research in Nursing administration from keywords. Method: Keywords of journals were extracted and the frequency of the appearance of each key words was sorted by a descending order. Results: A total of 327 key words were used. The most frequently used key words were 'Job satisfaction', 'Organizational commitment', 'Leadership'. Out of them, organizational culture, nursing performance, nursing classification, patient satisfaction, and ethics appeared most frequently in descending order. Conclusion: From the above it can be noted that many nursing administration concepts were handled in the papers. But there were not enough papers on the characteristics of the Nursing administration. It is suggested that in depth research be made on 'Nursing error', 'Nursing informatics', 'Web based learning'.

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

• PNF and Movement
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• v.16 no.3
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• pp.365-376
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• 2018

Purpose: This study investigates research trends in the Proprioceptive Neuromuscular Facilitation (PNF) and Movement journal. Methods: This study analyzes the frequency of keywords and their coincidences with medical subject headings (MeSH) over 15 years in 315 papers from volume 1, issue 1 to volume 15, issue 3 of a journal published by the Korean Proprioceptive Neuromuscular Facilitation Association. The research types and levels are also analyzed, and the journals are classified by subject, diagnosis, application of PNF, and technique used when PNF is applied. All of the variables are classified in five-year units and their trends are examined. Results: A total of 315 papers were published in 40 issues, and 1190 keywords were used over 15 years. The most frequently used keyword was "PNF." For the keywords that coincided with the MeSH, there were 235 (19.74%) complete coincidence words, 167 (14.03%) incomplete coincidence words, and 788 (66.21%) complete incoincidence words. Thus, the number of complete incoincidence words was the largest. For research types, there were 196 (61.90%) experimental studies, which was the most studied research type. For research levels, there were 155 (49.21%) Level 3 studies (non-randomized trial), which was the research level with the largest number of papers. Normal people were the most common subjects (121 cases, 38.41%), and the number of papers that did not use PNF was 187 (59.37%), which was larger than those that used PNF. The most frequently used combination technique was isotonics when PNF was used. Conclusion: Basic data on PNF-related research was obtained by analyzing papers published over the past 15 years. This information can be used to suggest future directions for PNF research.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.333-342
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• 2021

In this study, we analyzed the vocabulary, lemmas, keywords, and n-grams in 『Treasure Island』 to identify certain linguistic features of this British-American children's adventure novel. The current study found that, contrary to the popular claim that frequently-used words are important and essential to a story, the set of frequently-used words in 『Treasure Island』 were mostly function words and proper nouns that were not directly related to the plot found in 『Treasure Island』. We also ascertained that a list of keywords using a statistical method making use of a corpus program was not good enough to surmise the story of 『Treasure Island』. However, we managed to extract 30 keywords through the first quantitative keyword analysis and then a second qualitative keyword analysis. We also carried out a series of n-gram analyses and were able to discover lexical bundles that were preferred and frequently used by the author of 『Treasure Island』. We hope that the results of this study will help spread this knowledge among British-American children's literature as well as to further put forward corpus stylistic theory.

• Korean Journal of Acupuncture
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• v.39 no.1
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• pp.3-7
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• 2022

Objectives : The study's goal was to look at the research trends of articles published in the Korean Journal of Acupuncture. Methods : The Korean Journal of Acupuncture's website yielded a total of 882 articles. The VOSviewer application was used to visualize research trends from keywords. Results : A total of 87 keywords were found and visualized based on the average year of publication. The relevant characteristics and trends of basic acupuncture research published in Korean Journal of Acupuncture were determined by a network analysis based on the co-occurrences and publication year of keywords. Acupuncture, acupoint, herbal acupuncture, electroacupuncture, moxibustion, and meridian were the most frequently used keywords. Conclusions : This bibliometric study will give you a broad picture of research trends in Korean Journal of Acupuncture. These data may help to establish a timeline for the advancement of acupuncture basic research.

• Journal of Korean Library and Information Science Society
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• v.32 no.2
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• pp.307-322
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• 2001

The purpose of this study is to investigate how and which meta tags are used, which meta tags are used frequently, and what relationships there are between retrieval of WWW documents and meta tags. For the study, 1,000 WWW documents were selected and examined from OCLC NetFirst. The total of 92 meta tags was discovered and "description" and "keywords"meta tags were analyzed intensively. In addition, analysis of WWW documents showed that there are no significant relationships in meta tag usages between documents retrieved at the beginning and documents retrieved at the end. Comparative study between general internet search engines and commercial DBs such as NetFirst is suggested as a further study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.676-691
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• 2022

An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.