• Management & Information Systems Review
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• v.37 no.4
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• pp.41-62
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• 2018

As the volume of unstructured data increases through various social media, Internet news articles, and blogs, the importance of text analysis and the studies are increasing. Since text analysis is mostly performed on a specific domain or topic, the importance of constructing and applying a domain-specific dictionary has been increased. The quality of dictionary has a direct impact on the results of the unstructured data analysis and it is much more important since it present a perspective of analysis. In the literature, most studies on text analysis has emphasized the importance of dictionaries to acquire clean and high quality results. However, unfortunately, a rigorous verification of the effects of dictionaries has not been studied, even if it is already known as the most essential factor of text analysis. In this paper, we generate three dictionaries in various ways from 39,800 news articles and analyze and verify the effect each dictionary on the accuracy of document classification by defining the concept of Intrinsic Rate. 1) A batch construction method which is building a dictionary based on the frequency of terms in the entire documents 2) A method of extracting the terms by category and integrating the terms 3) A method of extracting the features according to each category and integrating them. We compared accuracy of three artificial neural network-based document classifiers to evaluate the quality of dictionaries. As a result of the experiment, the accuracy tend to increase when the "Intrinsic Rate" is high and we found the possibility to improve accuracy of document classification by increasing the intrinsic rate of the dictionary.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.565-573
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• 2017

The ex-post evaluation of governmental crisis management is an important issues since it is necessary to prepare for the future disasters and becomes the cornerstone of our success as well. In this paper, we propose a data cube model with data mining techniques for the analysis of governmental crisis management strategies and ripple effects of foot-and-mouth(FMD) disease using the online news articles. Based on the construction of the data cube model, a multidimensional FMD analysis is performed using on line analytical processing operations (OLAP) to assess the temporal perspectives of the spread of the disease with varying levels of abstraction. Furthermore, the proposed analysis model provides useful information that generates the causal relationship between crisis response actions and its social ripple effects of FMD outbreaks by applying association rule mining. We confirmed the feasibility and applicability of the proposed FMD analysis model by implementing and applying an analysis system to FMD outbreaks from July 2010 to December 2011 in South Korea.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.3 s.18
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• pp.1-9
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• 2005

The primary source of disaster information for victims as well as ordinary people is mass media. In spite of their importance, the media often inaccurately portrays reality, which has stimulated academic debates. In Korea, however, media reporting patters of disaster have been hardly addressed. Therefore, the paper analyzes how newspaper and television news have reported typhoon Rusa between August 29 and October 1 in 2002 by using KINDS(Korean Integrated News Database System). The results show that television news tend to present more soft news stories emphasizing human interest stories than newspaper articles, relying on victims as primary interviewees. It is also pointed out that the Korean media do not play a significant role in providing disaster information to public regarding how to lessen the effects of impact through preparation. Disaster mythology representing wrong beliefs about human behavior in disaster is found in Korean media reporting, too. Unlike their western counterparts, however, Korean media seem to use the dependency image of helpless victims in order to stimulate donations. Analyses of disaster reporting patterns suggest that, in make disaster warning messages associated with behavioral responses, credible and official sources should provide clear and precise warning messages to the media, and the media also need to stress individual responsibilities in protecting his or her own properties not to make victims heavily dependent on public supports, while inducing donations.

• Journal of Fashion Business
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• v.15 no.1
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• pp.158-170
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• 2011

The purpose of this study was to examine how recently fashion corporate did use SNS applications for their product promotion strategies as case studies, and to provide what kinds of SNS marketing strategies would be developed for fashion corporate. Specifically, this study was focused on Twitter among SNS applications. For this study, Internet webs, news paper, articles, and other press work were used for resources. Five fashion corporate such as Buckaroo, MLB, North Faces, Kolon, and ABC Mart were analyzed. As the results, first, fashion corporate used Twitter as the marketing tool for their product promotion. Second, they tried to make an increase the numbers of Twitter follower from their customers. Third, Twitter was used for making higher customer loyalty by fashion corporate through a variety of program such as special events, game, music, or viral marketing. However, there were still some limitations on fashion corporate's Twitter usage, compared to other non-fashion corporate. Thus, fashion corporate needs to provide more creative and unique Twitter marketing strategies. Therefore, based on these results, fashion brand merchandising marketing strategies of fashion products would be provided from this study.

• 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.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.191-198
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• 2019

This paper investigates different types of leadership found in foreign coaches in charge of the Korean national soccer team. To that end, news articles published during the tenure of those coaches were crawled for analysis. The analysis highlighted the following results. First, successful sports leaders showed their own specific types of leadership. Second, failed sports leaders showed specific types of leadership. The findings have the following implications. The leadership established based on the analysis results have practical implications in that they suggest the types of effectiveness leadership that are required of sports leaders in managing and leading athletes whilst generating tangible results and performance.

• Korean journal of communication and information
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• v.17
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• pp.112-144
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• 2001

This study attempts to analyze how and why Chosun Ilbo and Hankyoreh Shinmun produce particular social discourses about the media reform in different ways. In doing so, this paper attempts to disclose the ideological nature of media reform discourses in social contexts. For the purpose, a content analysis method was applied to the analysis of straight news, while an interpretive discourse analysis was appled to analyze both editorials and columns in newspapers. As a theoretical framework, an articulation theory was applied to explain the relationships among social forces, ideological elements, discourse practices and subjects to produce the media reform discourses. In doing so, I attempted to understand the overall conjuncture of the media reform aspects in social contexts. The period for the analysis was limited from January 10th to August 10th this year. Newspaper articles related to the media reform were obtained from the database of newspaper articles, "KINDS," produced by Korean Press Foundation, in searching the key word, "media reform". Total articles to be analyzed were 765, 429 from Hankyoreh Sinmun and 236 from Chosun Ilbo. The research results, first of all, empirically show that both Chosun Ilbo and Hankure Synmun used straight news for their firms' interests and value judgement, in selecting and excluding events related to media reform or in exaggerating and reducing the meanings of the events, although there are differences in a greater or less degree between two newspaper companies. Accordingly, this paper argues that the monopoly of newspaper subscriber by three major newspapers in Korean society could result in the forming of one-sided social consensus about various social issues through the distorting and unequal reporting by them. Second, this paper's discourse analysis related to the media reform indicates that the discourse of ideology confrontation between the right and the left produced by Chosen Ilbo functioned as a mechanism to realize law enforcement of the right in articulating the request of media reform and the anti-communist ideology. It resulted in the discursive effect of suppressing the request of media reform by civic groups and scholars and made many people to consider the media reform as a ideological matter in Korean society.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.633-647
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• 2018

Ramyeon is a wheat-food that has been mixed a dietary culture of China, Japan, American and Korea. Simultaneously it was the first convenience food that made of flour in Korea, which had been relatively few recipes until the early 1960's. To examine the changes of Ramyeon which has been consumed in large quantities since the late 1960s, this paper classified 1963~2012 into 6 periods. From political, economic, social, cultural and international perspectives we considered the changes of Ramyeon through the articles analysis of mass media(DongaIlbo, The KyunghyangShinmun, MBC news). The total number of articles was 3,823 with an average of 76.46/y. Each field was summarized as following sub-topics; In political(Election, North Korea), in economic(Price, Economy), in social(Promotion campaign for eating of flour, Incidents, Donation), in cultural(Consumption, Extreme situations, Nutrition) and in international field(Exporting). Each period was named according to the feature; Early stage, Expansion stage, Domestic growth stage, Overseas stage, Advanced stage, Transition stage. Ramyeon has changed in a closely relation with society for past 50 years.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.730-741
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• 2011

With the development of the internet and computer technology, the amount of information through the internet is increasing rapidly and it is managed in document form. For this reason, the research into the method to manage for a large amount of document in an effective way is necessary. The conventional document categorization method used only the keywords of related documents for document classification. However, this paper proposed keyword extraction method of based on association rule. This method extracts a set of related keywords which are involved in document's category and classifies representative keyword by using the classification rule proposed in this paper. In addition, this paper proposed the preprocessing method for efficient keywords creation and predicted the new document's category. We can design the classifier and measure the performance throughout the experiment to increase the profile's classification performance. When predicting the category, substituting all the classification rules one by one is the major reason to decrease the process performance in a profile. Finally, this paper suggested automatically categorizing plan which can be applied to hierarchical category architecture, extended from simple category architecture.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.