• The Korean Journal of Emergency Medical Services
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• v.24 no.1
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• pp.85-92
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• 2020

Purpose: The purpose of this study was to analyze the English abstract key words found within the Korea Society of Emergency Medical Services journal using text mining techniques to determine the adherence of these terms with Medical Subject Headings (MeSH) and identify key word trends. Methods: We analyzed 212 papers that were published from 2012 to 2019. R software, web scraping, and frequency analysis of key words were conducted using R's basic and text mining packages. Additionally, the Word Clouds package was used for visualization. Results: The average number of key words used per study was 3.9. Word cloud visualization revealed that CPR was most prominent in the first half and emergency medical technician was most frequently used during the second half. There were a total of 542 (64.9%) words that exactly matched the MeSH listed words. A total of 293 (35%) key words did not match MeSH listed words. Conclusion: Researchers should obey submission rules. Further, journals should update their respective submission rules. MeSH key words that are frequently cited should be suggested for use.

• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.162-172
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• 1998

In this paper, we implement a gigabyte Hangul text retrieval engine HMG(Hangul MG) which is based on the English text retrieval engine MG(Managing Gigabytes) and the Hangul lexical analyzer HAM(Hangul Analysis Module). To support Hangul information, we use the KSC 5601 code in the database construction and query processing stages. The lexical analyzer, parser, and index construction module of the MG system are modified to support Hangul information. To show the usefulness of HMG system, we implemented a NOD(Novel On Demand) system supporting the retrieval of Hangul novels on the WWW. The proposed system HMG can be utilized in the construction of massive full-text information retrieval systems supporting Hangul.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.935-947
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• 2016

This paper considers the analysis of patterns in meteorological research and development using a text-mining algorithm as the method of analyzing unstructured data. To analyze text data, we define a list of terms related to meteorological research and development, construct times series of a term-document matrix through data preprocessing, and identify terms that have upward or downward patterns over time. The proposed methodology is applied to multi-year plans funded by Korea Meteorological Administration research and development programs from 2011 to 2015.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.597-613
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• 2019

Purpose: The purpose of this study is to analyze the trend of quality research by analyzing the entire JKSQM(Journal of the Korean Society for Quality Management). Methods: This study is to analyze the frequency of words used in the abstract of the all JKSQM by applying the text mining processing. We use wordcrowd among text mining techniques. Results: 22 words of high frequency were presented in the abstract of the paper published in the JKSQM for 42 years. The frequency of words was shown on a 10 year basis, and the four important words were plotted on a change graph for each Vol. Frequent words of each Vol. are added in the appendix. Conclusion: The main research results are as follows. First, there has been no significant change in research trends over the last 40 years. Second, the early SQC words have been widely used, and since 1990, many words such as service-oriented words have been used, indicating a change in the times. Third, the use of the words of the 4th industrial revolution since 2010 is weak. In the above analysis, the trend of quality research in Korea is within the quality category and can be considered conservative. Now, it is expected that everything will be changed in the period of the 4th Industrial Revolution, and it is time to study the direction of quality in Korea.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.153-161
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• 2018

In this paper, we propose a text-centered approach to identify the research trend of thyroid cancer in Korea. We incorporate statistical analysis, text mining and machine learning techniques with our clinical insights to find connective associations between terminologies and to discover informative clusters of literatures. The incidence of thyroid cancer in Korea increased rapidly in the 2000s, which fueled the debate regarding overdiagnosis, but recently the number of patients undergoing surgery has decreased significantly due to conscious reform efforts from various circles. We analyzed the abstracts and keywords of related research papers from DBpia. It was found that most were case reports in the 1980s, and some papers in the 1990s discussed the early detection of thyroid cancer by mass screening. While many papers focused on different diagnostic techniques and the detection of small cancers in the 2000s, many emphasized more on the quality of life of patients in the 2010s. There was an apparent change in the topics of thyroid cancer research over past decades. The results of this study would serve as a reference guide for current and future research directions.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.45-61
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• 2019

Blockchain technology, which enables transparent transactions among individuals without central control, opens up diverse business possibilities. It is also expected that blockchain will have a ripple effect on the entire area of society including finance, manufacturing, distribution, and the public sector. Previous studies related to the blockchain also deals with its functional features and application to industrial and public fields. In the new technology such as blockchain, it is necessary to know what social perception is in order to create technological development environment, but there is a lack of research on it. Therefore, this study aims to find out the implications for industrial and policy direction by analyzing issues related to the blockchain in South Korea and the US through text mining. From these two countries, we collected text data related to blockchain in online communities and internet articles. Then, we did co-occurrence analysis and topic modeling on them respectively. As a result of this study, we have found common points and differences in keywords and topics extracted from social media in the two countries. Based on them, we can offer helpful suggestions for building a sound blockchain ecosystem, and directions for future research.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.717-734
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• 2022

Purpose: The purpose of this study is to effectively detect violations that occur simultaneously against Good Manufacturing Practice, which were concealed by drug manufacturers. Methods: In this study, we present an analysis framework for analyzing regulatory violation patterns using Association Rule Mining (ARM), Text Mining, and t-distributed Stochastic Neighbor Embedding (t-SNE) to increase the effectiveness of on-site inspection. Results: A number of simultaneous violation patterns was discovered by applying Association Rule Mining to FDA's inspection data collected from October 2008 to February 2022. Among them there were 'concurrent violation patterns' derived from similar regulatory ranges of two or more regulations. These patterns do not help to predict violations that simultaneously appear but belong to different regulations. Those unnecessary patterns were excluded by applying t-SNE based on text-mining. Conclusion: Our proposed approach enables the recognition of simultaneous violation patterns during the on-site inspection. It is expected to decrease the detection time by increasing the likelihood of finding intentionally concealed violations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.189-191
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• 2022

In modern society, messenger services are necessary to communication with others, and criminals are no exception. In representative cases of Burning Sun Gate(2018) and NthRoom(2019), messenger data analysis was used as a smoking gun to solve these criminal cases. Therefore messenger text analytics is critical for the resolution of crimes in a modern environment. also, it takes a lot of time to analyze messenger data in the digital forensic investigation process, so researchers in text mining need to be more effective to respond with the current situation In this paper, we study various natural language preprocessing(NLP) methods according to the characteristics of instant messages to effectively proceed with NLP analysis on instant messengers.

• Journal of Information Technology Services
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• v.22 no.2
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• pp.93-127
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• 2023

This study analyzed the CEO New Year's addresses of major Korean companies, extracting key topics for employees via text mining techniques. An intended contribution of this study is to assist reporters, analysts, and researchers in gaining a better understanding of the New Year's addresses by elucidating the implicit and implicative features of messages within. To this end, this study collected and analyzed 545 New Year's addresses published between 2012 and 2021 by the top 66 Korean companies in terms of market capitalization. Research methodologies applied include text clustering, word embedding of keywords, frequency analysis, and topic modeling. Our main findings suggest that the messages in the New Year's addresses were categorized into nine topics-organizational culture, global advancement, substantial management, business reorganization, capacity building, market leadership, management innovation, sustainable management, and technology development. Next, this study further analyzed the managerial significance of each topic and discussed their characteristics from the perspectives of time, industry, and corporate groups. Companies were typically found to emphasize sound management, market leadership, and business reorganization during economic downturns while stressing capacity building and organizational culture during market transition periods. Also, companies belonging to corporate groups tended to emphasize founding philosophy and corporate culture.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.529-536
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• 2023

The secondhand market in Korea has been showing steady growth. However, the number of fraud cases and the amount of damages from fraudulent activities in secondhand transactions are also increasing. As of 2021, the size of the secondhand market reached 24 trillion won, but the total amount of fraud-related damages reached 360.6 billion won. In order to prevent fraud between individuals, secondhand trading platforms have implemented a safety payment system. However, new types of fraud methods exploiting the safety payment system have emerged, undermining the security of secondhand transaction safety payments. In this study, we aim to utilize text mining to examine the current state of the safety payment system in secondhand transactions and propose improvement measures by analyzing the system through text mining and network analysis.