• 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 Digital Convergence
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• v.18 no.9
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• pp.181-190
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• 2020

The purpose of this study is to analyze the trends of wearable keywords using text mining methodology. To this end, 11,952 newspaper articles were collected from 1992 to 2019, and frequency analysis and bi-gram analysis were applied. The frequency analysis showed that Samsung Electronics, LG Electronics, and Apple were extracted as the highest frequency words, and smart watches and smart bands continued to emerge as higher frequency in terms of devices. As a result of the analysis of the bi-gram, it was confirmed that the sequence of two adjacent words such as world-first and world-largest appeared continuously, and related new bi-gram words were derived whenever issues or events occurred. This trend of wearable keywords will be useful for understanding the wearable trend and future direction.

• Journal of Elementary Mathematics Education in Korea
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• v.23 no.4
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• pp.459-479
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• 2019

The purpose of this study is to explore the research trends and knowledge structures of 『Journal of Elementary Mathematics Education in Korea』 by applying the keyword network analysis. To do this, we analyzed the frequency of the occurrence of keywords in the journal and conducted keyword network analysis using the Krkwic program and NodeXL program. The results of the analysis are as follows. Firstly, 749 keywords were extracted from keyword cleansing process and 48 keywords, including mathematics curriculum, mathematics textbooks, school mathematics, mathematical problem solving, mathematically gifted student, etc. appeared more than five times. Secondly, the keyword network analysis showed that the keywords-mathematics textbooks, school mathematics, mathematical problem solving, mathematical communications-have high connection centrality. Finally, we provided the limitations of this study and suggested future research.

• Proceedings of the Korean Society for Information Management Conference
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• 2012.08a
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• pp.143-146
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• 2012

본 연구에서는 MEDLINE에 수록된 논문의 암 관련 우울증 문헌을 대상으로 MeSH(Medical Subject Headings) 키워드를 이용하여 1992년부터 2011년까지 20년간의 연구 동향을 분석하였다. 암과 우울증에 해당하는 MeSH 키워드를 주제로 다룬 논문 3,389편과 50,778개의 키워드를 대상으로 주요 학술지 및 암 발병 위치와 우울증 치료요법을 분석하였다. 암 관련 우울증 논문의 암 발병 위치별 빈도는 유방암이 799편으로 가장 높았으며, 폐암, 전립선암, 뇌종양, 두경부암이 뒤를 이었다. 또한 우울증 치료 요법별 빈도는 비약물치료가 552편으로 약물치료 400편보다 높게 나타났으며 비약물치료는 크게 상담치료와 상담 외 치료에 대한 키워드로 구분되었고, 약물치료는 치료 요법명과 약명으로 다시 구분되었다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.4
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• pp.285-302
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• 2023

Based on this study, the research trends were identified for the field of public libraries in Korea by utilizing the keyword network analysis. For 20 years from 2003 to 2022, a total of 752 papers related to the public libraries published in the four largest academic journals in the field of library and information science in Korea were analyzed. The research results are as follows. First, from 2003 to 2022, an annual average of 37.6 papers were published, demonstrating a pattern of repeated rise and fall. Second, the keywords of 'service' and 'culture' were identified as the most discussed keywords as they were found to be among the top five in terms of the frequency of occurrence, connection centrality, and the mediation centrality analysis results. Third, in terms of the results of analyzing the co-occurrence frequency of keyword pairs, attention was paid to the keyword pairs of education-program, service-user, service-children, and service-disability.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.241-242
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• 2021

본 논문에서는 2020년 1월부터 2020년 8월까지 8개월간의 유통되었던 코로나 19와 관련된 뉴스 데이터를 이용하여 기간 및 지역별 단어의 빈도수를 구하고, 그 결과를 활용해 코로나 19와의 상관관계를 분석하고, 시각화하였다. 뉴스데이터는 한국언론진흥재단에서 운영하는 뉴스 빅데이터 시스템인 '빅카인즈'에서 수집된 데이터를 이용하였다. 본 논문에서 웹서비스를 활용해 시각화하였으며 지역과 기간을 선택하면 분석한 결과를 불러와 전체 지역대비 선택한 지역의 뉴스 빈도수, 선택한 지역의 주요 키워드, 주요 키워드의 지역별 일자별 변화 등을 보여주고 있다. 이러한 시각화를 통해 이전에 발생되었던 사건에 대해 주요 키워드와 코로나 19의 상관관계를 쉽게 파악을 할 수 있다.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.11-19
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• 2024

The purpose of this study was to analyze user reviews of running applications using text mining. This study used user reviews of Nike Run Club and Runkeeper in the Google Play Store using the selenium package of python3 as the analysis data, and separated the morphemes by leaving only Korean nouns through the OKT analyzer. After morpheme separation, we created a rankNL dictionary to remove stopwords. To analyze the data, we used TF, TF-IDF and LDA topic modeling in text mining. The results of this study are as follows. First, the keywords 'record', 'app', and 'workout' were identified as the top keywords in the user reviews of Nike Run Club and Runkeeper applications, and there were differences in the rankings of TF and TF-IDF. Second, the LDA topic modeling of Nike Run Club identified the topics of 'basic items', 'additional features', 'errors', and 'location-based data', and the topics of Runkeeper identified the topics of 'errors', 'voice function', 'running data', 'benefits', and 'motivation'. Based on the results, it is recommended that errors and improvements should be made to contribute to the competitiveness of the application.

• 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.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.