• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.125-136
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• 2016

Recently, many people switch from an open SNS like facebook to a closed SNS like Kakaostory to expect more empathetic responses. In this study, I derive the design factors that motivate empathetic responses in SNS. In particular, I focus on Kakaostory that is popular for women in their 30s and 40s. I conduct literature review and survey and find out the design factors and identified the type of photos that induce comments and feeling icons. While writing comments is influenced by the relationship with the poster and the contents, adding a feeling icons is affected by the states of the responders. The heavy users are more affected by the contents than by relationship. The photos with more responses have better image quality and capture situation better. The photos with more comments are cuter, brighter, and more positive. The photos with feeling icons only are visually sophisticated. This results can be useful to develop a design that induce empathetic responses.

• Journal of Elementary Mathematics Education in Korea
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• v.19 no.4
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• pp.625-647
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• 2015

How a pre-service teacher understands and comments on mathematics instruction can serve as the foundation of her teaching expertise. Given that prospective teachers observe demonstrative mathematics teaching implemented by an in-service teacher and make a comment on it during their practicum period, this paper specified the levels of their ability in commenting on mathematics instruction and explored the characteristics of such levels. It is significant that this paper provides a systematic and comprehensive analysis of such levels in terms of topic, agent, stance, evidence, and alternative perspective. The results of this study showed that the commenting levels may be classified by Level 1 (fragmentary), Level 2 (inspective), and Level 3 (analytical), and that the most frequent level of this study was at Level 2. Multiple regression analysis demonstrated that stance is the most influential in determining the levels of comments among their analytic components. An analysis of the participants' anecdotes showed that the experience of observing demonstrative teaching during the practicum may have impact on the belief of mathematics instruction and self-image as a teacher. Building on these results, this paper provides implications of teacher preparation programs to enhance prospective teachers' ability to analyze elementary mathematics lessons.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2484-2498
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• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.63-70
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• 2008

The authors, Tom Karygiannis of NIST, and Bernard Eydt, Greg Barber, Lynn Bunn, and Ted Phillips of Booz Allen Hamilton, wish to thank Steven Fick, Rick Korchak, Kate Remley, Jeff Guerrieri, Dylan Williams, Karen Scarfone, and Tim Grance of NIST, and Kenneth Waldrop and Beth Mallory of Booz Allen Hamilton. These individuals reviewed drafts of this document and contributed to its technical content. The authors would also like to express their thanks to several experts for their critical review and feedback on drafts of the publication. These experts include V.C. Kumar of Texas Instruments; Simson Garfinkel of the Naval Postgraduate School; Peter Sand of the Department of Homeland Security; Erika McCallister of MITRE; and several professionals supporting Automatic Identification Technology(AIT) program offices within the Department of Defense(DoD), especially Nicholas Tsougas, Fred Naigle, Vince Pontani, Jere Engelman, and Kathleen Smith. During the public comment period we received helpful comments from the following Federal Government agencies: the US Departments of Defense, Health and Human Services, Homeland Security, Labor, and State; the Office of the Director of National Intelligence; the Office of Management and Budget; and the General Services Administration. We also received several helpful contributions from commercial industry, including comments from EPCglobal, VeriSign, and Priway. Finally, the authors wish to thank the following individuals for their comments and assistance: Brian Tiplady, Daniel Bailey, Paul Dodd, Craig K. Harmon, William MacGregor, Ted Winograd, Russell Lange, Perry F. Wilson, John Pescatore, Ronald Dugger, Stephan Engberg, Morten Borup Harning, Matt Sexton, Brian Cute, Asterios Tsibertzopoulos, Mike Francis, Joshua Slob in, Jack Harris, and Judith Myerson.

• Korean Journal of Social Welfare
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• v.62 no.1
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• pp.391-415
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• 2010

Recently, there were some welfare embezzlement cases of public social workers. The purpose of this study is to explore public perceptions of public social workers by analysing the comments named "datgeul" in the internet media discussion rooms("toronbang") about welfare embezzlement cases of public social workers. The results show that the main discourse about public social workers to perform a dual role as the public servants and social workers on the front line of public social welfare is that they are the victims of both a public official system and welfare administration system. In addition, social workers in public sphere are still recognized as service personnels with sacrifice and commitment rather than as professionals. Finally, the implications of this study were discussed to improve public perceptions of social welfare professionals.

• Journal of Korean Academy of Nursing
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• v.49 no.6
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• pp.736-747
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• 2019

Purpose: This study aimed to explore public opinion on workplace bullying in the nursing field, by analyzing the keywords and topics of online news comments. Methods: This was a text-mining study that collected, processed, and analyzed text data. A total of 89,951 comments on 650 online news articles, reported between January 1, 2013 and July 31, 2018, were collected via web crawling. The collected unstructured text data were preprocessed and keyword analysis and topic modeling were performed using R programming. Results: The 10 most important keywords were "work" (37121.7), "hospital" (25286.0), "patients" (24600.8), "woman" (24015.6), "physician" (20840.6), "trouble" (18539.4), "time" (17896.3), "money" (16379.9), "new nurses" (14056.8), and "salary" (13084.1). The 22,572 preprocessed key words were categorized into four topics: "poor working environment", "culture among women", "unfair oppression", and "society-level solutions". Conclusion: Public interest in workplace bullying among nurses has continued to increase. The public agreed that negative work environment and nursing shortage could cause workplace bullying. They also considered nurse bullying as a problem that should be resolved at a societal level. It is necessary to conduct further research through gender discrimination perspectives on nurse workplace bullying and the social value of nursing work.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.101-106
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• 2016

A number of non-structured data associated with lectures in the field of university education have been generated and it is an important consideration of the students's written comments lecture evaluation. The purpose of this study is to find student interaction factors associated with the student evaluation of teaching at universities, and to provide some insights into improving the student evaluation program based on the results. So, this study consists of three steps that create interaction score, collect student's written comments satisfaction, and analyze an individual professor score. There are a number of limitations to this study. The limitation is that the study was conducted on a narrow sample of the overall student population.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.399-406
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• 2019

The purpose of this study is to examine the relationship between corporate reputation and the profitability. In this study, Big Data Analysis was conducted for Hyundai Motor, Shinsegae Department Store, SK Telecom, and Amorepacific to solve research problems. The results of this study show that the effect of each corporate reputation on the profitability is different according to the company. For products such as Hyundai Motor and Amorepacific that are used directly by consumers, the corporate reputation formed by the comments was more influential. In addition, distribution Service company such as Shinsegae Department Store showed more influence by online news. On the other hand, SK Telecom did not have a significant effect on profitability. Based on the results, this study emphasizes the importance of online news and comments on corporate reputation management, and aims to contribute to establishing an efficient reputation management strategy by examining the relationship between corporate reputation and profitability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.