• Journal of the Korean Institute of Oriental Medical Informatics
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• v.15 no.2
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• pp.7-56
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• 2009

Objective: The purpose of this study is to compare the original texts with quotations in 16 Kinds of Cough Part in Haesu Chapter of Dong-Yi-Bo-Gam and to find out the ideas of Huh Jun(許浚, 1546-1615; the author of Dong-Yi-Bo-Gam) in there. Methods: I compared the original texts with quotations in 16 Kinds of Cough Part in Haesu Chapter of Dong-Yi-Bo-Gam. Results: 1. There is only one quoted sentence which perfectly matches with original text in 16 Kinds of Cough Part in Haesu Chapter of Dong-Yi-Bo-Gam. The other sentences are all modified while they are quoted by Huh Jun, at least one word. 2. The arrangement order of 'medical effect', 'consisting medicines and their dosages' and 'doctrine in application' were rearranged following the form of Dong-Yi-Bo-Gam when being quoted. 3. In cases of reciting the text, Huh Jun tries to clarify the original source of the context. However, instead of using original quotations he recited rephrased quotes from other sources. 4. Huh Jun cites from not only cough parts of other texts but also asthma(喘症) or heat(積熱) parts. 5. Titles of original text books are recorded in the end of all sentences of Dong-Yi-Bo-Gam, but there are a few wrong titles recorded. Conclusion: In consideration of the above-mentioned, the Dong-Yi-Bo-Gam is not the mere collection of various Oriental Medical books, but the Classic of Oriental Medicine to hold Huh Jun's own opinion.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.285-290
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• 2010

인터넷이 대중화됨에 따라 누구나 쉽게 자신의 의견을 온라인상에 표현할 수 있게 되었다. 그 결과 생각이나 느낌을 나타내는 의견 데이터들의 양이 급속도로 방대해졌으며, 이러한 데이터들을 이용한 여러 응용 사례들의 등장으로, 효율적인 검색 및 자동 분류 기술이 요구되고 있다. 이런 기술적 흐름에 맞추어 의견 데이터 분류에 관한 여러 연구들이 이루어져 왔다. 이러한 의견 분류에 대한 연구들을 살펴보면, 분류를 위해 자질(Feature)로서 사용한 단일어(Single word)가 아닌 2개 이상의 N-gram 단어, 어휘 구문 패턴 및 통사 구문 패턴 등을 사용한다. 특히, 패턴은 단일어나 N-gram 단어에 비해 유연하고, 언어학적으로 풍부한 정보를 표현할 수 있기 때문에 이를 주요 연구 주제로 사용되었다. 그럼에도 불구하고, 이러한 연구들은 주로 영어에 대한 연구들이었으며, 한국어에 패턴을 적용하여 주관성을 갖는 문장을 분류하거나, 극성을 분류하는 연구들은 아직 미비하다. 한국어의 특색으로 한국어는 용언의 활용이 발달되어 있어, 어미의 변화가 다양하며, 그 변화에 따라 의미가 미묘하게 변화한다. 그러나 기존 한국어에 대한 의견 분류 연구들은 단어의 핵심 의미만을 파악하기 위해 어미 부분을 제거하고 어간만을 취해서 처리하여 어미에 대한 의미변화를 고려하지 못하므로 분류 정확도가 영어권에 연구 결과에 비해 떨어진다. 그래서 본 연구는 영어에 적용된 패턴을 이용한 기존 방법들을 정리하고, 그 방법들 중에서 극성을 지닌 문장성분 패턴을 한국어에 적용하였다. 그리고 어미의 변화에 대한 패턴을 추출하여 이 변화가 의견 분류의 성능에 미치는 영향을 분석하였다.

• Acta Via Serica
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• v.3 no.2
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• pp.151-160
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• 2018

There is a variety of opinions about the first translation activities within the Turkic Empire. It is widely believed that some Buddhist sutras were translated into the Turkic language in the period of Taspar Qagan (572-581). This theory is based on certain arguments: Some Turks practiced Buddhism, Buddhist monks translated sutras in the center of the Turkic Empire, Taspar brought sutras from China and had them translated, and the monarch of Northern Qi had a sutra translated and sent to Taspar. However, in my opinion, these arguments lack credibility. This article, which is based on primary Chinese sources, will question the likelihood of such translation activities having occurred. Some Chinese records for these claims exist: Da Tang Nei Dian Lu (大唐內典錄) and Xu Gao Seng Chuan (續高僧傳) by the Buddhist monk Jinagupta and the records of Hui Lin in Sui Shu (隋書) and Wen Xian Tong Kao (文獻通考). These are known as "primary sources." Secondary sources, namely contemporary history and language studies, such as those in books and articles, must be based on primary sources. It can be seen that claims relating to the first Turkic translation activities at the time of Taspar are mainly derived from secondary sources, and that the arguments in these secondary sources vary. Sometimes researchers make suppositions on the existence of information that is not referred to in primary sources. However, this is not normal practice. If a researcher relies on unknowns for the evidence of information existing, it can cause false information, ideas and anachronisms to be created. It is important that primary sources, such as the Chinese sources mentioned above, be translated correctly in language and history studies. If only a word is mistranslated, very different results may occur. Mistranslating or misinterpreting a primary source allows conclusions to be reached that are not supported by dissemination of information from primary sources. This can mislead experts and result in information that is not correct being considered as being true. As well as helping to prevent such misinterpretations occurring, another aim of this paper is to question the interpretations of the first Turkic translations in contemporary studies on history and language. The origin of such assessments will be explored and the validity of that information will be examined.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.9-21
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• 2008

In this paper, we revisit a generally accepted opinion: implementing Elliptic Curve Cryptosystem (ECC) over GF$(2^m)$ on sensor motes using small word size is not appropriate because partial XOR multiplication over GF$(2^m)$ is not efficiently supported by current low-powered microprocessors. Although there are some implementations over GF$(2^m)$ on sensor motes, their performances are not satisfactory enough due to the redundant memory accesses that result in inefficient field multiplication and reduction. Therefore, we propose some techniques for reducing unnecessary memory access instructions. With the proposed strategies, the running time of field multiplication and reduction over GF$(2^{163})$ can be decreased by 21.1% and 24.7%, respectively. These savings noticeably decrease execution times spent in Elliptic Curve Digital Signature Algorithm (ECDSA) operations (Signing and verification) by around $15{\sim}19%$.

• Journal of Home Health Care Nursing
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• v.29 no.3
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• pp.278-287
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• 2022

Purpose: To analyze Korean nurse-related channels and video titles on YouTube, the world's largest online video sharing and social media platform, to clarify public opinion and image of nurses. We seek utilization strategies and measures through current status analysis. Methods: Data is collected by crawling video information related to Korean nurses, and correlation is analyzed with frequent word analysis and keyword network analysis. Results: Through the YouTube algorithm, 2,273 videos of 'Nurse' were analyzed in order of recent views, relevance, and rating, and 2,912 videos searched for with the keyword 'Nurse + Hospital, COVID-19, Awareness, University, National Examination' were analyzed. Numerous videos were uploaded, and nursing work that was uploaded in the form of a vlog recorded a high number of views. Conclusion: We could see if the YouTube video shows images of nurses. It has been confirmed that various information is being exchanged rather than information just for promotional purposes.

• Journal of People, Plants, and Environment
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• v.23 no.2
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• pp.233-243
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• 2020

This study collects social big data used in various fields in the past 9 years and explains the patterns of major keywords of the arboretums and botanical gardens to use as the basic data to establish operational strategies for future arboretums and botanical gardens. A total of 6,245,278 cases of data were collected: 4,250,583 from blogs (68.1%), 1,843,677 from online cafes (29.5%), and 151,018 from knowledge search engine (2.4%). As a result of refining valid data, 1,223,162 cases were selected for analysis. We came up with keywords through big data, and used big data program Textom to derive keywords of arboretums and botanical gardens using text mining analysis. As a result, we identified keywords such as 'travel', 'picnic', 'children', 'festival', 'experience', 'Garden of Morning Calm', 'program', 'recreation forest', 'healing', and 'museum'. As a result of keyword analysis, we found that keywords such as 'healing', 'tree', 'experience', 'garden', and 'Garden of Morning Calm' received high public interest. We conducted word cloud analysis by extracting keywords with high frequency in total 6,245,278 titles on social media. The results showed that arboretums and botanical gardens were perceived as spaces for relaxation and leisure such as 'travel', 'picnic' and 'recreation', and that people had high interest in educational aspects with keywords such as 'experience' and 'field trip'. The demand for rest and leisure space, education, and things to see and enjoy in arboretums and botanical gardens increased than in the past. Therefore, there must be differentiation and specialization strategies such as plant collection strategies, exhibition planning and programs in establishing future operation strategies.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.7
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• pp.371-378
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• 2017

Recently, with the spread of smart device and the introduction of web services, the data is rapidly increasing online, and it is utilized in various fields. In particular, the emergence of social media in the big data field has led to a rapid increase in the amount of unstructured data. In order to extract meaningful information from such unstructured data, interest in big data technology has increased in various fields. Big data is becoming a key resource in many areas. Big data's prospects for the future are positive, but concerns about data breaches and privacy are constantly being addressed. On this subject of big data, where positive and negative views coexist, the research of analyzing people's opinions currently lack. In this study, we compared the changes in peoples perception on big data based on unstructured data collected from the social media using a text mining. As a results, yearly keywords for domestic big data, declining positive opinions, and increasing negative opinions were observed. Based on these results, we could predict the flow of domestic big data.

• English Language & Literature Teaching
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• no.5
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• pp.49-62
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• 1999

Education systems throughout the world encourage their students to learn languages other than their native one. In Australia, our Education Boards provide students with the opportunity to learn European and Asian languages. French, German, Chinese and Japanese are the most popular languages studied in elementary and high schools. This choice is a reflection of Australias European heritage and its geographical position near Asia. In most non-English speaking countries, English is the foreign language most readily available to students. In Korea, the English language is actively promoted by the Education Department and, in less official ways, by companies and the public. It is impossible to be anywhere in Korea without seeing the English language alongside or intermingled with Korean. When I ask students why they are learning English, I receive answers that include the word globalization and the importance of English throughout the world. When I press further and ask why they personally are learning English, the students mention passing exams, usually high school tests or TOEIC, and the necessity of passing the latter to obtain a good job. Seldom do I ever hear anything about communication: about the desire to talk with other people in English, to read novels or poetry in English, to understand movies or pop-songs in English, to chat on the Internet in English, to search for information on the Internet in English, or to email pen-pals in English. Yet isnt communication the only valid reason for learning a language? We learn our native language to communicate with those around us. Shouldnt we set the same goal for learning a foreign language? In my opinion communication, whether it is reading and writing or speaking and listening, must be central to language learning. Learning a language to pass examinations is meaningless unless those examinations are a reliable indicator of the ability of the student to communicate. In previous eras, most communication in a foreign language was through reading novels or formal letters. This required a thorough knowledge of grammar and a large vocabulary. Todays communication is much less formal. Telephone conversations, tele-conferences, faxes and emails allow people to communicate regularly and informally. Reading materials are also less formal as popular novels and newspapers are available world-wide. Movies and popular songs have added to the range of informal communication available. Finally travel has ensured that people from different cultures will meet easily and regularly. This informal communication requires less emphasis on grammar and vocabulary and more emphasis on comprehension and confidence to speak. Placing communication central to language learning has important implications for the Education system and for teachers.