• 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.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.19-25
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• 2018

With the development of IT technology, various services such as artificial intelligence and autonomous vehicles are being introduced, and many changes are taking place in our lives. However, if secure security is not provided, it will cause many risks, so the information security becomes more important. In this paper, we analyzed the research trends of main themes of information security over time. In order to conduct the research, 'Information Security' was searched in the Web of Science database. Using the abstracts of theses published from 1991 to 2016, we derived main research topics through topic modeling and time series regression analysis. The topic modeling results showed that the research topics were Information technology, system access, attack, threat, risk management, network type, security management, security awareness, certification level, information protection organization, security policy, access control, personal information, security investment, computing environment, investment cost, system structure, authentication method, user behavior, encryption. The time series regression results indicated that all the topics were hot topics.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.61-68
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• 2022

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.269-279
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• 2022

Though easy payment service is the most well accepted one among various fin-tech services, the users still face difficulties and feel embarrassed when they use it. Over the past few years, many studies have been done on the users' experiences of easy payment service but there are little studies directly exploring the users' inquiries on the web. In this paper, we analyzed users' questions on Kakao Pay, Naver Pay, and Samsung Pay in Naver Jisik-iN, the biggest inquiry service in Korea from 2019 to 2020. We used keyword analysis, association analysis, and sentiment analysis. We found out that each payment service has distinct inquiries from the users according to its platform which it is based on.

• Journal of Information Science Theory and Practice
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• v.5 no.3
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• pp.31-47
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• 2017

The narrow-down approach, separately composed of search and classification stages, is an effective way of dealing with large-scale hierarchical text classification. Recent approaches introduce methods of incorporating global, local, and path information extracted from web taxonomies in the classification stage. Meanwhile, in the case of utilizing path information, there have been few efforts to address existing limitations and develop more sophisticated methods. In this paper, we propose an expansion method to effectively exploit category path information based on the observation that the existing method is exposed to a term mismatch problem and low discrimination power due to insufficient path information. The key idea of our method is to utilize relevant information not presented on category paths by adding more useful words. We evaluate the effectiveness of our method on state-of-the art narrow-down methods and report the results with in-depth analysis.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.35-39
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• 2002

광고성 메일이 개인 당 하루 평균 10통 내외로 오며, 그 제목만으로는 광고메일을 효율적으로 제거하기 어려운 현실이다. 이러한 어려움은 주로 광고 제목을 교묘히 인사말이나 답신처럼 변경하는 데에서 오는 것이며, 이처럼 제목으로 광고를 삭제할 수 없도록 은폐하는 노력은 계속될 추세이다. 그래서 제목을 통한 변화에 적응하면서, 제목뿐만 아니라 내용에 대한 의미 파악을 자동으로 수행하여 스팸 메일을 차단하는 방법이 필요하다. 본 연구에서는 정상 메일과 스팸 메일의 범주화(classification) 방식으로 접근하였다. 이러한 범주화 방식에 대한 기준을 자동으로 알기 위해서는 사람처럼 문장 해독을 통한 의미파악이 필요하지만, 기계가 문장 해독을 통해서 의미파악을 하는 비용이 막대하므로, 의미파악을 단어수준 등에서 효율적으로 대신하는 text mining과 web contents mining 기법들에 대한 적용 및 비교 연구를 수행하였다. 약 500 통에 달하는 광고메일을 표본으로 하였으며, 정상적인 편지군(500 통)에 대해서 동일한 기법을 적용시켜 false alarm도 측정하였다. 비교 연구 결과에 의하면, 메일 패턴의 가변성이 너무 커서 wrapper generation 방법으로는 해결하기 힘들었고, association rule analysis와 link analysis 기법이 보다 우수한 것으로 평가되었다.

• The Journal of Korean Association of Computer Education
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• v.5 no.2
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• pp.61-68
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• 2002

The development of internet technology has brought many changes in education as well as in overall society and especially, web-based instructions have been rapidly increasing. Unlike the conventional education environment, many web-based instructions offer multimedia effects with text, graphics, sound, animation, video, and so on via internet. In addition, by using the hypermedia functionality, they help students to navigate anywhere they want to go. However, the web-based instructions have been evaluated by the same criteria as the conventional courses. This paper develops the course evaluation items for web-based instruction and compares the results of course evaluation for the web-based instructions and the conventional courses.

• Journal of Korean Library and Information Science Society
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• v.34 no.2
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• pp.233-253
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• 2003

Knowledge organization is the way we humans understand the world. There are two types of information organization mechanisms studied in information retrieval: namely classification md clustering. Classification organizes entities by pigeonholing them into predefined categories, whereas clustering organizes information by grouping similar or related entities together. The system of the Internet information resources extracts a keyword from the words which appear in the web document and draws up a reverse file. Term clustering based on grouping related terms, however, did not prove overly successful and was mostly abandoned in cases of documents used different languages each other or door-way-pages composed of only an anchor text. This study examines infometric analysis and clustering possibility of web documents based on co-link topology of web pages.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.271-274
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• 2019

In this paper, we proposed a detailed address acquisition scheme for automatically collecting data of a web page in a frame that is difficult to access by a general web access method. Using the Python language and the Beautiful Soup library, which can utilize the proposed address resolution technique and the HTML selector, we were able to automatically collect all the bulletin board text data written in several pages. By using the proposed method, we can collect large amount of data automatically by Python web scraping program for web pages of any form of address, and we expect that it can be used for big data analysis.