• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.548-558
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• 2019

Along generalization of internet news comments, malicious comments have been spread and made many social problems. Because writings reflect human mental state or trait, analyzing malicious comments, human mental states could be inferred when they write internet news comments. In this study, we analyzed malicious comments of English and Korean speaker using LIWC and KLIWC. As a result, in both English and Korean, malicious comments are commonly more used in sentence, word phrase, morpheme, word phrase per sentence, morpheme per sentence, positive emotion words, and cognitive process words than normal comments, and less used in the third person singular, adjective, anger words, and emotional process words than normal comments. This means people are state that they can not control their feeling such as anger and can not think well when they write news comments. Therefore, when internet comments were written, service provider should consider the way that commenters monitor own writings by themselves and that they prevent the other users from getting close to comments included many negative-emotion words. In other sides, it is discovered that English and Korean malicious comments was discriminated by authenticity. In order to be more objective, gathering data from various point of time is needed.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2945-2951
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• 2009

By observing and classifying the articles that are centering around the forecasting information which are dealing with the health related articles in the mass media such as the daily press, KBS1 9 O'clock news, and TV broadcast station's health serialization program, this research was to fulfill ultimately to present health predicted execution for the data on the present state of analysis for the general public to acquire the health related information and to practice the presented basic data of the useful health information to the patients and general public by understand the tendency of the health related information that is presented to the general public. The period of the subject for analysis conducted in a year, started in January 1st, 2006 and finished in December 31st 2006. The research sampled about 50% of the subject of analysis by using the computer's random sampling in considering the quantity of work. Look in to the subject of health information, the daily news paper illustrated in the order of the cause of diseases and dangerous factor (15.5%), the medical treatment and techniques, the medication(15.4%), and the health promotion(14.6%), and the TV news presented the subject on the cause of diseases and dangerous factor(27.5%) the most, and the least presented in the order of the mechanics(24.2%), and the administrator(11.3%).

• Asia Marketing Journal
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• v.16 no.1
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• pp.21-33
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• 2014

When the negative event is published, the company tends to go through the negative impact on the firm performance. Especially, with the SNS, the negative event is instantly spread on indefinite region so the impact seems bigger than the period before the SNS media appearance. It seems that everyone considers the SNS media impact on the firm performance quite big. However, there has been no empirical study on the impact comparison on the firm performance between pre and post SNS media occurrence periods. This study tries to empirically compare the impact of the negative event on the firm performance between pre and post SNS media appearance. Our study starts fromthe basic but not verified question; Does really the negative event have more negative impact in the post-SNS-occurrence period than in the pre-SNS-occurrence period? In order to examine the impact of the negative publicity on firm performance in two eras, pre and post SNS media appearance, we used CAR (Cumulative Abnormal Resturns) model. By using this model, we could verify the statistical significance of cumulative abnormal returns in market between before and after the events. For event samples, we focused on food manufacturers and collected the negative events from 1991 to 2003 for pre-SNS occurrence period, and from 2010 to 2013 for post-SNS occurrence period. Based on the listed food companies at KOSPI, we researched Naver News Library (newslibrary.naver.com) and Naver News (news.naver.com) for all the individual negative events published for both periods. Firm returns data were collected from TS 2000 (KOCO Info) and market portfolio data were collected from KRX Exchange. Through our empirical analysis, our finding is interesting to note that the type of events differently influences on the firm performance. With the SNS, the health-related events have influence on the firm performance 'after the event day' whereas the company behavior trust events have influence 'before the event day'. Our findings have implications for management. When a negative event directly related to or threatening customers or their life such as health, it is crucial to fix up the situation right after the event occurs. On the other hand, when a negative event is not publicly available information such as company behavior trust, it is important for marketers to strengthen the firms' trust reputation and control the bad WOM before the event.

• Informatization Policy
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• v.31 no.1
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• pp.3-31
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• 2024

This study aimed to examine public concerns in South Korea considering the country's unique context, triggered by the advent of generative artificial intelligence such as ChatGPT. To achieve this, comments from 102 YouTube video news related to ethical issues were collected using a Python scraper, and morphological analysis and preprocessing were carried out using Textom on 15,735 comments. These comments were then analyzed using a Correlated Topic Model (CTM). The analysis identified six primary topics within the comments: "Legal and Ethical Considerations"; "Intellectual Property and Technology"; "Technological Advancement and the Future of Humanity"; "Potential of AI in Information Processing"; "Emotional Intelligence and Ethical Regulations in AI"; and "Human Imitation."Structuring these topics based on a correlation coefficient value of over 10% revealed 3 main categories: "Legal and Ethical Considerations"; "Issues Related to Data Generation by ChatGPT (Intellectual Property and Technology, Potential of AI in Information Processing, and Human Imitation)"; and "Fear for the Future of Humanity (Technological Advancement and the Future of Humanity, Emotional Intelligence, and Ethical Regulations in AI)."The study confirmed the coexistence of various concerns along with the growing interest in generative AI like ChatGPT, including worries specific to the historical and social context of South Korea. These findings suggest the need for national-level efforts to ensure data fairness.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.27-40
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• 2024

Objective : This study aimed to conduct a systematic review of the assessment tools used to assess the housing environment of older adults. Methods : Data were collected from January 2015 to August 31st, 2023, by searching databases including the Cochrane Library, PubMed, and ProQuest. From the 267 articles, nine assessment tools were selected for analysis based on their original instruments. These tools were categorized and systematically organized for analysis based on their frequency of use, assessment purposes, sub-domains, scales, and other relevant criteria. Results : Among the nine tools, HOME FAST and IPAQ-E were the most frequently used (20% each). The objectives of these tools are to assess friendliness, physical barriers, fall prevention, dementia-friendly environments, physical activity, and accessibility. The measurement scope encompassed various factors, such as outdoor spaces, buildings, transportation, housing, and community support. Conclusion : When considering the suitability of housing for the older adults population, providing foundational data for the rational selection of evaluation tools with logical validity is important. This includes factors such as the objectives and measurement scopes of housing environment assessment tools.

• MALSORI
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• no.43
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• pp.1-9
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• 2002

This study aims to model Korean prosodic phrasing using CART(classification and regression tree) method. Our data are limited to Korean read speech. We used 400 sentences made up of editorials, essays, novels and news scripts. Professional radio actress read 400sentences for about two hours. We used K-ToBI transcription system. For technical reason, original break indices 1,2 are merged into AP. Differ from original K-ToBI, we have three break index Zero, AP and IP. Linguistic information selected for this study is as follows: the number of syllables in ‘Eojeol’, the location of ‘Eojeol’ in sentence and part-of-speech(POS) of adjacent ‘Eojeol’s. We trained CART tree using above information as variables. Average accuracy of predicting NonIP(Zero and AP) and IP was 90.4% in training data and 88.5% in test data. Average prediction accuracy of Zero and AP was 79.7% in training data and 78.7% in test data.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.83-89
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• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.118-127
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• 2020

The paper introduces trends and methodological challenges of quantitative Korean text analysis by using the case studies of academic and news media articles on "migration" and "immigration" within the periods of 2017-2019. The quantitative text analysis based on natural language processing technology (NLP) and this became an essential tool for social science. It is a part of data science that converts documents into structured data and performs hypothesis discovery and verification as the data and visualize data. Furthermore, we examed the commonly applied social scientific statistical models of quantitative text analysis by using Natural Language Processing (NLP) with R programming and Quanteda.