• 산업경영시스템학회지
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• 제46권3호
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

• 인터넷정보학회논문지
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• 제19권2호
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• pp.49-56
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• 2018

Internet news consumption is rapidly growing in Korea, and majority of that is being done through Naver, Korea's primary search engine. Naver is also the go-to search engine for smartphone use. This study analyzed 824 most popular news accessed via mobile gears; the news items were selected from Naver's 'Daily Top 10 Stories,' dating from March 2016 to December 2016. The results indicate that entertainment news were the most viewed, while political and social issue news were the most liked and commented by mobile users. With regard to news value, 'prominence' and 'impact' were the two most important factors that influenced a user's news selection process in a mobile environment. The degree of a news' 'prominence' was the most important factor that determined the number of views, while 'impact' was critical to determining "the most commented-upon" and "the most liked" news. The results also indicate that mobile news consumers prefer more dramatic storylines and events that incite public anger or grief, threaten the safety of citizens, or evoke emotional sympathy rather than 'hard news' about such subjects as politics and economics.

• 정보관리연구
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• 제41권2호
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• pp.161-181
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• 2010

본 연구는 국내 주요 10개 경제연구기관의 보고서를 대상으로 인터넷 뉴스에서의 인용 행태를 분석하고 각 기관의 영향력을 h-지수와 다양한 변형 지수(g-지수, $h_s$-지수, $g_s$-지수)로 제시하였다. 이를 위해 네이버 뉴스에서 기사를 검색하여 총 878건에 대한 기사의 내용 분석을 실시하였다. 먼저, 보고서 인용 기사 수, 뉴스매체, 주제 섹션, 신속성, 정확성, 중심성, 기사 길이 등을 중심으로 인용행태를 분석하였다. 다음으로 인용 건수로 영향력을 산출하여 기관 순위를 비교하였다. 분석 결과 인용 지수 순위가 상위권에 속한 기관들은 지수 간의 순위 차이가 거의 없었고, 전문가가 제시한 순위와도 유사한 반면, 중 하위권의 기관들은 상대적으로 지수에 따라 차이가 나고, 전문가 순위와 다른 양상을 보였다.

• 디지털융복합연구
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• 제15권7호
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• pp.383-391
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• 2017

본 연구는 카드뉴스와 스트레이트 뉴스 형태 차이에 따른 호감도, 이해도, 신뢰도, 만족도 및 지속사용 의도에 대한 수용자의 태도를 알아보기 위해 232명을 대상으로 설문조사를 실시하였다. 연구결과 첫째, 카드 뉴스는 스트레이트 뉴스보다 이용자가 이해하기 쉽고, 지속적으로 사용하고자 하는 욕구도 강했다. 둘째, 뉴스에 대한 이용자들의 태도 요인은 서로 간에 정(+)의 방향으로 영향을 주고 있었는데 호감도, 이해도, 신뢰도는 만족도에 영향을 미치며, 만족도는 다시 지속사용 의도에 영향을 미쳤다. 이 영향의 강도는 카드 뉴스와 스트레이트 뉴스가 각각 달랐는데 카드 뉴스에서 만족도에 영향을 미치는 정도는 호감도, 이해도, 신뢰도 순으로 강했으며, 스트레이트 뉴스는 신뢰도, 호감도, 이해도 순으로 영향력이 강했다. 본 연구 결과는 수용자에게 선택받는 뉴스를 생산해 정보의 전달력과 파급력을 높여 뉴스의 정보 제공 기능을 강화할 수 있는 방법을 찾아가는데 실증적인 자료가 될 것이다.

• 통상정보연구
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• 제10권1호
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• pp.395-414
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• 2008

The objective of this study is to introduce the concepts and theories of conditional heteroscedastic volatility models and the news impact curves and apply them to the Korea inbound tourism market. Three volatility models were introduced and used to estimate the conditional volatility of monthly arrivals of inbound tourists into Korea and news impact curves according to the three models. Results of this study are as follows. As the proportion of American tourists occupied a large amount of Korea inbound tourism market, the markets' forecasting is very important. The news impact curves which used EGARCH model (1,1) and TGARCH model(1,1), with data on these tourists to Korea showed an asymmetry effect of volatility. It was common that bad news means that it was estimated more sensitively than good news. From these results, we will notice that American tourists who visited Korea only for tourism are affected by good news. The result suggests that the Korea government and tourism industry should pay more attention to changes in the tourism environment following bad news because conditional volatility increases more when a negative shock occurs than when a positive shock occurs.

• 한국IT서비스학회지
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• 제18권1호
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• 대기
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• 제30권3호
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• Journal of Information Technology Applications and Management
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• 제21권4호
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• pp.157-171
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• 2014

As electronic word of mouth plays an important role in purchase behavior among consumers, the number of studies on the impact of electronic word of mouth is rapidly increasing. Nevertheless, it is difficult to discover comparative studies on the mass media which had a great impact on consumer's purchase behavior before the impact of electronic word of mouth becomes greater versus the social media where electronic words of mouth are created and distributed. It is considered that it seems to be necessary to find an appropriate mutual supplement point between the media designed for a successful marketing by comparing and analyzing the existing mass media versus the social media, major media for electronic word of mouth. Therefore, this study aims to compare and analyze the impact of comments on movie revenue in the representative forms of mass media such as online news and social media blogs. In particular, this study also considers an appropriate media for promoting movies by period by comparing and analyzing the two media before and after film release. For analysis, this study collects the information on the number of comments on online news and blogs in 70 Korean movies released in 2011 and 2012 from five weeks before film release to eight weeks after film release on a daily basis via Naver. This study also collects the information on the movie revenue using the statistical data of movie industry from Korean Film Commission. As a result of empirical data analysis, it is found that the two media showed no difference in movie revenue before film release, but after film release, the impact of blogs was more significant than that of online news.

• 자원ㆍ환경경제연구
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• 제13권2호
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• pp.175-194
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• 2004

본 논문은 예상하지 못한 뉴스충격이 유가의 변동성에 미치는 영향이 비대칭임을 밝힘과 더불어 유가의 변동성을 가장 정확히 추정할 수 있는 변동성모형을 결정하는데 연구의 목적을 둔다. 여기에는 GARCH모형, EGARCH모형, AGARCH모형, GJR모형과 같은 네 가지 변동성모형이 이용된다. 변동성모형을 선정하기에 앞서 부호편의검정과 규모편의검정을 통해 모형의 설정오류를 조사한 후, GARCH모형은 비대칭효과를 보이는 AGARCH모형과 GJR모형에 비해 나쁜 뉴스에 대해서는 과소평가를, 좋은 뉴스에 대해서는 과대평가를 하는 경향이 있음을 보인다. 그리고 EGARCH모형은 GARCH모형, GJR모형, AGARCH모형에 비해 좋은 뉴스와 나쁜 뉴스에 대해 조건부 분산을 지나치게 높거나 낮게 평가하며, 특히 나쁜 뉴스에 대해서는 이해하기 어려울 정도로 높게 평가함을 보인다. 또한 AGARCH모형은 GARCH모형보다 나쁜 뉴스를 낮게 평가하며, EGARCH모형은 GARCH모형보다 좋은 뉴스를 높게 평가하기 때문에 유가의 변동성을 설명하는 데 GJR모형이 적합함을 밝힌다.

• 인터넷정보학회논문지
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• 제19권4호
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• pp.123-133
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• 2018

이 연구는 모바일 뉴스의 핵심 소비층으로 부상하고 있는 밀레니얼 세대가 모바일과 SNS를 통해 어떤 뉴스콘텐츠와 뉴스가치를 선호하며, 뉴스기사에 대하여 어떻게 반응하는지를 조사 분석하였다. 연구결과, 밀레니 세대가 뉴스를 가장 많이 소비하는 SNS 창구는 카카오톡과 페이스북이었으며, 하루에 평균 한 번 이상 SNS를 통해 뉴스를 소비하는 것으로 나타났다. 밀레니얼들은 인구학적 특성별로 뉴스유형 선호와 뉴스기사에 대한 반응정도가 달랐으며, 뉴스주제별로 반응정도도 다르게 나타났다. 또한 밀레니얼들의 뉴스 소비 경로는 SNS가 전국일간지와 지상파TV, 인터넷신문, 종편채널 등보다 높게 나타났으며, 선호하고 중요하게 생각하는 뉴스가치로는 '영향성'과 '유용성'이 '속보성'이나 '흥미성'보다 높게 평가되었다.