• Journal of Korean Medical classics
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• v.21 no.3
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• pp.285-296
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• 2008

"The Golden Mirror of Medicine(醫宗金鑑)" is a very famous medical book. It was compiled by the medical officers of the Cheong(淸, Qing) Dynasty, especially headed by Ogyeom(吳謙, Wuqian). "The Elimination ＆amp; Supplement about the Famous Prescription Comments(刪補名醫方論)"that forms one part of "The Golden Mirror of Medicine(醫宗金鑑)", is a collection of famous notes about important prescriptions. Many parts of "The Elimination ＆amp; Supplement about the Famous Prescription Comments(刪補名醫方論)" were quoted from "The Famous Prescription Comments on Ancient and Modern Times(古今名醫方論)". The first part of the vol. Ⅱ of "The Elimination ＆amp; Supplement about the Famous Prescription Comments(刪補名醫方論)"mainly describes about the prescriptions of elevating Yanggi[升陽], which were invented by Igo(李杲, Ligao), a famous physician of the Geum(金, Jin) Dynasty. The treatments were five and three of them were quoted from "The Famous Prescription Comments on Ancient and Modern Times(古今名醫方論)". The basic rule of herbal combination of these prescriptions is a combination of herbs of strengthening-gi[補氣] and dispersing herbs[發散藥]. If the dispersing herbs[發散藥] is used with the herbs of strengthening-gi[補氣藥], they elevate Yanggi(陽氣) together.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.157-181
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• 2013

Using user satisfaction surveys and LibQUAL+ instruments, libraries are increasingly gathering qualitative data such as verbatim user comments as well as quantitative data. Such qualitative data can be utilized as clues in establishing library service strategies: to better understand user issues, to identify areas for service improvement, and to prioritize user needs. For this, it is necessary to analyze user comments data and to apply results to the delivery of service and the library policies. This study is an attempt to investigate ways in which user comments data can be made useful in libraries. It identifies different methods of analyzing user comments data from LibQUAL+ surveys and compares qualitative data analysis software programs and taxonomies. It also presents the results of applying these tools to a subset of actual user comments data gathered from a recent LibQUAL+ survey at a major university library in Korea.

• Journal of Korean Medical classics
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• v.20 no.1
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• pp.65-74
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• 2007

"The Golden Mirror of Medicine(醫宗金鑑)" was compiled by the medical officers of the Qing government, headed by Wu-Qian(吳謙)."The Elimination &Supplement about the Famous Prescription Comments(刪補名醫方論)", a collection of notes written by famous scholars, is one part of "The Golden Mirror of Medicine(醫宗金鑑)" . Through the translation and comparative study of invigorative prescription(補氣方劑) in "The Elimination & Supplement about the Famous Prescription Comments(刪補名醫方論)" of "The Golden Mirror of Medicine(醫宗金鑑)" with "The Famous Prescription Comments on Ancient and Modem Times(古今名醫方論)" , we confirmed that many sentences of "The Famous Prescription Comments on Ancient and Modern Times(古今名醫方論)" were quoted, but that most of the text was not quoted as the same: it was revised and supplemented. In organization, invigoration prescriptions are placed at the head of the chapter, indicating their importance. In classification, prescriptions for reinforcing middle-jiao(中焦) and replenishing qi(補中益氣湯類 方劑) were usually included in the invigoration prescriptions(補氣方劑), which is explained in chapter II, thereby clearly distinguishing between treatments of deficiency of qi(氣虛) and combining it with the sinking of qi of middle-jiao(中氣下陷).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5321-5334
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• 2019

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on "place" and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

• International Journal of Contents
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• v.9 no.2
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• pp.22-26
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• 2013

Comparing online and paper-based environment for report submission and correction, the former supersedes to the latter, since (1) the turn-around time becomes shorter, (2) teaching opportunity increases, and (3) as a consequence, the student's achievement level becomes higher in the online environment. In this paper, we propose an annotation tool GABA for PDF document in order to reduce correction time by the teachers and to facilitate instruction to students. In a usual class, the same or similar assignments are given to the students. Then it is often the case that many students make similar mistakes. A teacher can register and classify common correction comments to GABA. Report correction time becomes significantly shorter by reusing the registered comments. GABA also provides various support functions in order to assist efficient checking of numerous report files such as (1) sorting of frequently-used comments, (2) similarity-based file sorting, and (3) cross tabulation of comments using category and weight.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.9
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• pp.1525-1534
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• 2018

Objective: The aim of this study was to examine the use of outcome-based observations within Assured Dairy Farm scheme (ADF), Soil Association Organic Standards (SA), and cross compliance (CC) farm assessment reports. Methods: A total of 449 ADF reports, 37 SA reports and 26 CC reports were analyzed and their objective comments categorized as either resource-based or outcome-based. Results: A mean of 61.0% of ADF questions were responded to with comments, in comparison to 25.0% of SA and, 21.0% of CC report questions. The SA and CC reports had significantly more outcome-based comments than the ADF (p<0.001). The assessors' tendency of choosing resource-based approach was revealed in the questionnaire results. Conclusion: Generally, the comments were comprehensive and contained professional judgements. Large numbers of comments provided in the ADF reports were mostly compliant and resource-based evidence, which serves as proof of assessment rather than aiding the certifying process. The inclusion of specific welfare outcome measures in the SA inspection likely increased the use of outcome-based comments in the reports, irrespective of whether the farm achieved compliance with a given standards. The CC scheme, on the other hand, focused on providing outcome-based evidence to justify noncompliant decisions.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.1013-1019
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• 2021

This paper reviewed the temporal changes in the public opinions on nuclear energy in Korea with a big data analysis of nuclear energy-related articles and their comments posted on the portal site NAVER. All articles that included at least one of "nuclear energy," "nuclear power plant (NPP)," "nuclear power phase-out," or "anti-nuclear" in their titles or main text were extracted from those posted on NAVER in January 2010-December 2019. First, we performed annual word frequency analysis to identify what words had appeared most frequently in the articles. For that period, the most frequent words were "NPP," "nuclear energy," and "energy." In addition, "safety" has remained in the upper ranks since the Fukushima NPP accident. Then, we performed sentiment analysis of the pre-processed articles. The sentiment analysis showed that positive-tone articles have been reported more frequently than negativetone over the entire analysis period. Last, we performed sentiment analysis of the comments on the articles to examine the public's intention regarding nuclear issues. The analysis showed that the number of negative comments to articles each month-irrespective of positive or negative tone-was always larger than that of positive comments over the entire analysis period.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.173-187
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• 2012

As newspapers which have been major news media are being replaced by on-line news media in recent years, many researchers are paying attention to "comments(news users' short remarks on an article)", a newly emerged way of forming public opinion. This study is examining how the similarity between political disposition of on-line news visitors and that of news media impacts upon their evaluation on quality of comments from the viewpoint of 'social identity theory.' This study may have academic significance because it inspected the pattern of media usage and the cognition of comments in relation to political disposition for the first time and showed 'comments reading' and the function of comments to form public opinion(comments journalism).

• The Journal of Bigdata
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• v.4 no.2
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• pp.35-45
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• 2019

Recently, the damage with social cost of malicious comments is increasing. In addition to the news of talent committing suicide through the effects of malicious comments. The damage to malicious comments including abusive language and slang is increasing and spreading in various type and forms throughout society. In this paper, we propose a technique for detecting abusive language using a bi-directional long short-term memory neural network model. We collected comments on the web through the web crawler and processed the stopwords on unused words such as English Alphabet or special characters. For the stopwords processed comments, the bidirectional long short-term memory neural network model considering the front word and back word of sentences was used to determine and detect abusive language. In order to use the bi-directional long short-term memory neural network, the detected comments were subjected to morphological analysis and vectorization, and each word was labeled with abusive language. Experimental results showed a performance of 88.79% for a total of 9,288 comments screened and collected.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.197-206
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• 2018

The harmful effects of online malicious comments are continuously increasing. Many previous studies have confirmed that neutralization of malicious comments is a key predictor. Neutralization is theoretically composed of seven multidimensional concepts, and the significance of neutralization factors varies depending on the type of deviant behavior. This study focuses on the fact that the malicious comment researches have considered the neutralization techniques in a single dimension as opposed to demonstrating the multidimensional neutralization techniques in the deviant behavior research. On the other hand, the role of arbitrator in deviant behavior can contribute to restraining deviant behavior, but the research of intervention intention is relatively lacking in malicious comments research. This study, composed of two complementary studies, tried to find out the related factors of malicious comments and intervention intention. As a result of study, This study revealed that malicious commentator uses the neutralization techniques of condemn the condemners and denial of responsibility. In addition, we found that affective empathy has a significant effect on the intervention intention in malicious comments.