• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.889-896
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• 2012

In this paper, we propose a statistic to measure investor sentiment. It is a usual phenomenon that an asymmetric volatility (referred to as the leverage effect) is observed in financial time series and is more sensitive to bad news rather than good news. In a bubble state, investors tend to continuously speculate on financial instruments because of optimism about the future; subsequently, prices tend to abnormally increase for a long time. Estimators of the transformation parameter and the skewness based on Yeo-Johnson transformed GARCH models are employed to check whether a bubble or abnormality exist. We verify the appropriacy of the proposed interpretation through analyses of KOSPI and NIKKEI.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.339-342
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• 2012

Nowadays, Spoken Dialogue System is rapidly growing by investing a lot from researches as well as organizations. One of concrete evidences is that the appearance of commercial systems such as Siri, SVoice, DARPA, CLASSiC, GSearch etc. Moreover, Spoken Dialogue System is widely believed to be the future direction of software development. In Spoken Dialogue System, users interact to software by using their own voice instead of use their hands, keyboard, and mouse. This paper continuously presents our development of the Spoken Dialogue System on News. Particularly, we propose detailed design such as semantic concepts, semantic frames, slots, and so on for applying Hidden Vector State Model into our Spoken Dialogue System for Spoken Language Understanding.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.75-100
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• 2018

Mobile devices have become an important channel for news content usage in our daily life. However, online news content readers' resistance to online news monetization is more serious than other digital content businesses, such as webtoons, music sources, videos, and games. Since major portal sites distribute online news content free of charge to increase their traffics, customers have been accustomed to free news content; hence this makes online news providers more difficult to switch their policies on business models (i.e., monetization policy). As a result, most online news providers are highly dependent on the advertising business model, which can lead to increasing number of false, exaggerated, or sensational advertisements inside the news website to maximize their advertising revenue. To reduce this advertising dependencies, many online news providers had attempted to switch their 'free' readers to 'paid' users, but most of them failed. However, recently, some online news media have been successfully applying the Pay-What-You-Want (PWYW) payment model, which allows readers to voluntarily pay fees for their favorite news content. These successful cases shed some lights to the managers of online news content provider regarding that the PWYW model can serve as an alternative business model. In this study, therefore, we collected 379 online news articles from Ohmynews.com that has been successfully employing the PWYW model, and analyzed the comparative importance of systematic attributes of online news content on readers' voluntary payment. More specifically, we derived the six systematic attributes (i.e., Type of Article Title, Image Stimulation, Article Readability, Article Type, Dominant Emotion, and Article-Image Similarity) and three or four levels within each attribute based on previous studies. Then, we conducted content analysis to measure five attributes except Article Readability attribute, measured by Flesch readability score. Before conducting main content analysis, the face reliabilities of chosen attributes were measured by three doctoral level researchers with 37 sample articles, and inter-coder reliabilities of the three coders were verified. Then, the main content analysis was conducted for two months from March 2017 with 379 online news articles. All 379 articles were reviewed by the same three coders, and 65 articles that showed inconsistency among coders were excluded before employing conjoint analysis. Finally, we examined the comparative importance of those six systematic attributes (Study 1), and levels within each of the six attributes (Study 2) through conjoint analysis with 314 online news articles. From the results of conjoint analysis, we found that Article Readability, Article-Image Similarity, and Type of Article Title are the most significant factors affecting online news readers' voluntary payment. First, it can be interpreted that if the level of readability of an online news article is in line with the readers' level of readership, the readers will voluntarily pay more. Second, the similarity between the content of the article and the image within it enables the readers to increase the information acceptance and to transmit the message of the article more effectively. Third, readers expect that the article title would reveal the content of the article, and the expectation influences the understanding and satisfaction of the article. Therefore, it is necessary to write an article with an appropriate readability level, and use images and title well matched with the content to make readers voluntarily pay more. We also examined the comparative importance of levels within each attribute in more details. Based on findings of two studies, two major and nine minor propositions are suggested for future empirical research. This study has academic implications in that it is one of the first studies applying both content analysis and conjoint analysis together to examine readers' voluntary payment behavior, rather than their intention to pay. In addition, online news content creators, providers, and managers could find some practical insights from this research in terms of how they should produce news content to make readers voluntarily pay more for their online news content.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.167-173
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• 2020

This study aims to analyze the number and trend of news media news through timely analysis of how the articles about airlines and employees show changes before and after Corona19 in the situation where the world economy faces various problems due to the global pandemic of Corona19. For this purpose, the number of articles and trends related to airlines and employees were analyzed and visualized before and after Corona19 using the Korea Press Foundation Bigkinds news analysis service. For this purpose, the Bigkinds service system was extracted from January 1, 2019 to May 31, 2019 and from January 1, 2020 to May 31, 2020. The results of the analysis showed that the number of articles before and after Corona 19 exploded when aviation related events occurred. And it was confirmed that the trend is changing due to the restructuring news. Government and airlines will need to make active efforts to overcome the crisis in the aviation industry due to the impact of Corona 19. The results of this study are significant in that it analyzed the number and trends related to news articles before and after Corona 19, and suggested practical implications for establishing strategies for the future impacts on airlines and employees.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.176-182
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• 2018

In order to understand and track the trends of construction safety accident, this study shows the topic trends in the construction safety accident with LDA(Latent Dirichlet Allocation)-based topic modeling method for data analytics. Especially, it performs to figure out the main issue of construction safety accident with unstructured data analysis based on the topic modeling rather than a variety of structured data analysis for preventing to safety accident in construction industry. To apply this methodology, I randomly collected to 540 news article data about construction accident from January 2017 to February 2018. Based on the unstructured data with the LDA-based topic modeling, I found the 10 topics and identified key issues through 10 keyword in each 10 topics. I forecasted the topic issue related to construction safety accident based on analysis of time-series trends about the news data from January 2017 to February 2018. With this method, this research gives a hint about ways of using unstructured news article data to anticipate safety policy and research field and to respond to construction accident safety issues in the future.

• Journal of the Korean Society of Costume
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• v.22
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• pp.23-44
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• 1994

The purpose of this study was to clarify the cultural characteristic of dress and adornments by examining articles on dress adornments and related items in Korean newspaper over periods historically and objectively by means of content analysis. This study attempted to at-tain a macro-cultural view by analysing to at-tain a macro-cultural view by analysing closely the cultural characteristics of dress and adornments as a micro-cultural system through culturally based model suggested by hamilton. The two-hundreds and eighty articles on dress and adornments were selected from newspapers(most by form Chosun Ilbo and partly from Maeil Shinbo) pulished between 1920 and 1990. The results were as follows: 1. The culture of dress and adornment received much attention during the 1930's and 1960's and little during 1950's. 2. Various cultural characteristics of dress and adornments appeared on and after 1960's: reporting more foreign news items showing foreign-oriented and future-oriented features showing cultural relativism. In the 1920's and 1970's the contents of news items on dress and adornments show the most common- mass- oriented character. Foreign-oriented cultural tendencies in cloth-ing were increasing during from 1960's to 1970's but the tendencies were turned to rather tradition-oriented features on and after 1980's compare with 1960's-1970's. Advisory critical articles on dress and adornments were small in number and insignificant but compare with other periods these received much atten-tion during the 1920's and 1980's. 3. Ideological components received much at-tention on and after 1920's to 1990. Techo-nological components received much attention during 1920's and little during 1960's. The social structural components received a little attention on and after 1920's-1940's and 1990. 4. News items on women's dress and adornments received much attention from the 1920's to 1960's and news items on both men's and women's dress and adorments were in-creasing and received much attention on and after the 1970's. 5. The pragmatic cultures were mostly re-lated to techonological components and evaluative-normative culture were mostly re-lated to ideological and social structural components. In the light of these results dress and adorments as a cultural sub-system comprise a dynamic inteacting system that articulated directly with the macro-cultural system.

• 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 Industrial Convergence
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• v.21 no.1
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• pp.167-173
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• 2023

As research has been published to predict future data using regression analysis or artificial intelligence as a method of analyzing economic indicators. In this study, we designed a system that predicts prospective futures prices using artificial intelligence that utilizes topic probability data obtained from past news articles using topic modeling. Topic probability distribution data for each news article were obtained using the Latent Dirichlet Allocation (LDA) method that can extract the topic of a document from past news articles via unsupervised learning. Further, the topic probability distribution data were used as the input for a Long Short-Term Memory (LSTM) network, a derivative of Recurrent Neural Networks (RNN) in artificial intelligence, in order to predict prospective futures prices. The method proposed in this study was able to predict the trend of futures prices. Later, this method will also be able to predict the trend of prices for derivative products like options. However, because statistical errors occurred for certain data; further research is required to improve accuracy.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.55-68
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• 2023

Purpose: The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. Methods: After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.