• Journal of the Korean Society of Safety
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• v.38 no.5
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• pp.27-35
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• 2023

Social interest in disasters is a significant factor in shaping disaster management policies, enabling the enhancement of disaster safety management and prevention activities according to the specific needs of society. However, in the past, there were limits to measuring which disasters society was particularly interested in. Hence, in this study, a method of measuring social interest using news data was devised. Specifically, we classifed news reports into natural and social disasters, creating a comprehensive Social Interest Index (SII) on disasters covering from 2011 to 2021. Additionally, we quantitatively compared the SII with budgets allocated to disaster-related efforts. Our primary findings are as follows: First, our methodology not only distinguishes natural disasters from social disasters but also identifies emerging areas of societal concern. Second, in recent years (2014-2021), social disasters gained more attention than natural disasters. Third, the disaster safety budget accounted for approximately 3.5% of Korea's total budget, closely paralleling the SII we measured. However, exceptions were noted in cases such as heavy snow, cold waves, and heat waves, where the SII remained high, but the disaster safety budget was relatively low, indicating potential outliers. The findings of this research are projected to contribute to the improvement of national disaster management policies by providing a quantitative measure of social interest in disaster, enabling more informed and effective policy decisions.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Korea Entertainment Industry Association
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• v.15 no.6
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• pp.179-191
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• 2021

Social anxiety continues due to the emergence and spread of covid-19 infections. In this situation, false information related to the covid-19 infection is distributed through SNS in the form of fake news, which is a stumbling block to overcoming the national crisis. This study tried to present a theoretical basis for the establishment of policies for the regulation and eradication of fake news circulated through SNS by examining the effect of social anxiety on the fake news acceptance attitude by focusing on the use degree of SNS. For this study, a survey of 380 university students in the Seoul metropolitan area was conducted, and 336 data collected among them were analyzed as SPSS 25.0 and AMOS 23.0. According to the analysis results, social anxiety has a positive effect on the fake news acceptance attitude and the use degree of SNS, also the use degree of SNS has a positive effect on the fake news acceptance attitude. In addition, social anxiety has been confirmed to have a positive effect on fake news acceptance attitude through the use degree of SNS. These results empirically confirm the relationship between social anxiety, fake news acceptance attitude, and the use degree of SNS.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.3
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• pp.123-155
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• 2022

This study focuses on the management of the news production system in broadcasting companies. This paper concentrates on the process of data registration and metadata management in order to examine whether the currently produced news can have value as a 'public record' in the long term, and whether reliable and accurate information is preserved. In addition, the user experience in the current system is analyzed through in-depth interviews with Ingest Managers, Editors, and Archive Managers, who are closely related to metadata creation compared to other members of the its News Department. Finally, a sustainable metadata quality management method is sought to increase the value of news footage as a 'public record'. In this study, these points can be found out: the metadata of the news agency footage is input manually according to the user's will or working style, that is, the user-friendly metadata input system is insufficient. Accordingly, it can be seen that the quality of the metadata of the news video continues to deteriorate. As an alternative to overcome this, it is found that work flow improvement, system improvement, classification system and metadata improvement plan, etc. are definitely necessary in the short and long term.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.217-220
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• 2007

In this paper, we present a new anchor shot detection system which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) anchor shot detection module using a support vector data description. According to our computer experiments, the proposed system shows not only the comparable accuracy to the recent other results, but also more faster detection rate than others.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.421-425
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• 2000

The efficient researching of the News Video is require the development of video processing and editing technology to extract meaningful information from the Video data. The advanced information nations are researching the Video Editing System and recently they are concerned to research the perfect practical system. This paper represents the System that can extract and edit the meaningful information from the Video Data by the User demand through the Scene change detection and Editing system by the automatic/ passive classification and this system represents more efficient scene change detection algorithm which was selected by the user.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.133-138
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• 2008

In this paper, we propose a novel anchor shot detection system, named to MASD (Multi-phase Anchor Shot Detection), which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) one class SVM module for determining the anchor shots using a support vector data description. Besides the qualitative analysis, our experiments validate that the proposed system shows not only the comparable accuracy to the recently developed methods, but also more faster detection rate than those of others.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2062-2066
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• 2022

Nuclear energy sources are indispensable in cost effectively achieving carbon neutral economy, where public opinion is critical to adoption as the consequences of nuclear accident can be catastrophic. In this context, discussion on spent nuclear fuel is a prerequisite to expanding nuclear energy, as it leads to the issue of radioactive waste disposal. Given the dearth of study on spent nuclear fuel public acceptance, we use text mining and big data analysis on the news article and public comments data on Naver news portal to identify the Korean public opinion on spent nuclear fuel. We identify that the Korean public is more interested in the nuclear energy policy than spent nuclear fuel itself and that the alternative energy sources affect the position towards spent nuclear fuel. We recommend relating spent nuclear fuel issue with nuclear energy policy and environmental issues of alternative energy sources to further promote spent nuclear fuel.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.183-184
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• 2022

본 연구는 장애인 평생교육 관련 언론사 뉴스 빅데이터를 한국언론재단의 빅카인즈(BIGKinds) 시스템을 이용하여 분석하였다. 본 연구에서는 2000년 1월 1일부터 2020년 12월 31일까지 20년간, 총 54개 언론사에서 보도한 '장애인 평생교육' 관련 뉴스 기사들을 추출하였다. 그 분석대상 뉴스 빅데이터를 대상으로 키워드 트렌드 분석, 언어 네트워크 지도 구현, 연관어 분석(워드클라우드 제시) 등을 진행하였다. 본 연구 결과는 장애인 평생교육 관련 정책 입안 연구 및 실증적인 연구(평생교육 참여 요인 및 효과 등)의 기초자료로 활용될 수 있을 것으로 기대된다.