• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Korean journal of communication and information
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• v.53
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• pp.73-99
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• 2011

This study compares blogs of professional reporters with those of citizen reporters in terms of watchdog and interactivity functions. Watchdog function was analyzed in light of the extent of soft news and the degree of critical relationship with the government. Interactivity was assessed by analyzing readers' comments on the articles of reporter blogs. for citizen journalism and for professional one were chosen in order to minimize any discrepancies caused by ideological differences. As a result of the content analyses, citizen reporter blogs were found to deliver soft news more frequently than those of , whereas the former had stronger tendency to maintain the critical relationship with the government than the latter. As for the interactivity function, although the number of comments uploaded to citizen reporter blogs was higher than that of professional reporter blogs, the latter was found to meet the standards of communicative interaction more adequately than the former.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.189-199
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• 2015

This study made use of 9 types of utilizing idle farmland deducted from existing research in order to examine cases by type of idle farmland, and selected representative cases by type after analyzing contents of 165 available newspaper articles. Selected Cases were assorted into agricultural use and nonagricultural use, and agricultural use are as followed. (1)Community Service/Farming type is the case of Gimhae city reported on 'Busan Ilbo', (2)High Income/Farming type is the case of cooperative unit of Geumjeong crude drug in Yeongam appeared in 'Newsis', and the case of Omija industry in Mungyeong reported on 'Hankyoreh', (3)Tourism/Landscape/Farming type is the case of rape flowers and buckwheat flowers in Gupo village reported on 'Asia News Agency', (4)Stock Raising/Farming type is the case of growing foraging crops published in 'The Daejeon Ilbo', (5)Weekend farm type is the case of utilizing idle farmlands and creating weekend farm reported on 'Mediawatch', (6)High income/Forest type is creating Mulberry cultivation areas in Hamyang-Gun published in 'Yonhap News', (7)Ecology/Landscape/Forest type is forestation project of idle land reported on 'Newsis', (8)Agricultural Experience Study type is the case of managing agricultural experience study center in Dosun elementary center published in 'Sisajeju' and the case of non-agricultural application case, (9)Ecological Environment type is the case of wetland restoration of idle farmland in Gochang. This study investigated and arranged detailed contents by the literature search and interview investigation according to investigating items such as utilizing area, main agent, purpose, utilizing item, utilizing content, etc. by case. With that, it deducted implications as well as case characteristics, and finally suggested political proposals through the case analysis.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.2
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• pp.287-304
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• 2005

The goal of this research is to evaluate the usability of visually browsing the automatically extracted information. A domain-independent information extraction system was used to extract information from news type texts to populate the visually browasable knowledge base. The information extraction system automatically generated Concept-Relation-Concept triples by applying various Natural Language Processing techniques to the text portion of the news articles. To visualize the information stored in the knowledge base, we used PersoanlBrain to develop a visualization portion of the user interface. PersonalBrain is a hyperbolic information visualization system, which enables the users to link information into a network of logical associations. To understand the usability of the visually browsable knowledge base, IS test subjects were observed while they use the visual interface and also interviewed afterward. By applying a qualitative test data analysis method. a number of usability Problems and further research directions were identified.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.74-85
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• 2022

Along with the change in the values of marriage and the prevalence of non-marriage in Korean society, a new form of family composition called unmarried birth or non-maritalism childbirth has appeared, and social discussion in taking place in connection with the problem of a decrease in the birthrate. Using sentiment analysis and social network analysis, this research explored how the people's sentiment and perception has changed toward 'nonmarital birth.' The data used is comments on news articles from the period of November 2020 to August 2021. As a result of the study, there were a lot of positive comments during the social issue period by marriage, whereas there were many negative comments from the policy agenda to the policy making period. As a result of co-occurrence network analysis, the topic of family norm, policy, and personal aspect appeared. This study is significant in that it revealed that negative perceptions prevailed during the policy-making process after the issue of unmarried births after the issue of unmarried births, and it became a cornerstone of social discussion on unmarried births

• Journal of Sport and Applied Science
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• v.8 no.1
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• pp.11-17
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• 2024

Purpose: The purpose of the study was to understand a trend of esports in terms of gamers' and fans' perceptions toward esports using social big data. Research design, data, and methodology: In this study, researchers first selected keywords related to esports. Then a total of 10,138 buzz data created at twitter, Facebook, news media, blogs, café and community between November 10, 2022 and November 19, 2023 were collected and analyzed with 'Textom', a big data solution. Results: The results of this study were as follows. Firstly, the news data's main articles were about competitions hosted by local governments and policies to revitalize the gaming industry. Secondly, As a result of esports analysis using Textom, there was a lot of interest in the adoption of the Hangzhou Asian Games as an official event and various esports competitions. As a result of the sentiment analysis, the positive content was related to the development potential of the esports industry, and the negative content was a discussion about the fundamental problem of whether esports is truly a sport. Thirdly, As a result of analyzing social big data on esports and the Olympics, there was hope that it would be adopted as an official event in the Olympics due to its adoption as an official event in the Hangzhou Asian Games. Conclusions: There was a positive opinion that the adoption of esports as an official Olympic event had positive content that could improve the quality of the game, and a negative opinion that games with actions that violate the Olympic spirit, such as murder and assault, should not be adopted as an official Olympic event. Further implications were discussed.

• 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.

• Journal of the Korean Society of Costume
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• v.64 no.1
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• pp.1-17
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• 2014

The purpose of this study is to suggest the plans, which can creatively utilize Korean images by introducing the SCAMPER technique, one of the techniques used to create ideas in the fashion design process. For the research methods, the case study which collects all the masters' theses relating to the fashion design process applied Korean images for recent 10 years and the literature study through books, masters' theses, academic journals' theses, news articles, and Internet data were done together. As for the result of the study, the level of utilization of Korean images in the researches on Korean fashion designs stayed simple utilization, which used a motif as it is in its original form or changed only the size. Furthermore, of the 39 theses reviewed in the study, 59% of them used less than 3 of the 7 SCAMPER items. Therefore, it could be said that the Korean images are restrictively being used in the fashion design studies. If SCAMPER is to be actively utilized as a design process in the future, it is expected that it will make great contributions to the development of creative Korean fashion design.

• International Journal of Costume and Fashion
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• v.15 no.2
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• pp.37-47
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• 2015

The purpose of this study was to examine the differences in terminologies used in South and North Korea, to describe objects or activities related to clothing and textiles, as a part of a bigger project that aims at developing an educational program in provision of reunification of the Koreas. In this study, a total of 176 North Korean terms that differ from South Korean terms were collected from various sources, including dictionaries that are developed to compare South-North Korean languages as well as texts such as magazines and news articles, about North Korean daily life. The terms were classified into sub-categories: materials for clothing, clothing management, construction and design, garment names, body parts, description of physical appearance or state of hygiene, and apparel industry. Many of the North Korean terms were derived from native expressions, rather than adopting foreign terms or terms in Chinese characters. Some North Korean terms did not have any corresponding words in South Korean terms or vice versa. We expect the terminology list to become a useful educational resource in establishing a clothing and textiles curriculum in preparation of reunification, by allowing the students to familiarize with the differences in the usage of terms.