• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.175-194
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• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• International Journal of Contents
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• v.15 no.4
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• pp.65-73
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• 2019

Social media is a massive dataset in which individuals' thoughts are freely recorded. So there have been a variety of efforts to analyze it and to understand the social phenomenon. In this study, Twitter was used to define the moments when negative perceptions of the Korean Meteorological Administration (KMA) were displayed and the reasons people were dissatisfied with the KMA. Machine learning methods were used for sentiment analysis to automatically train the implied awareness on Twitter which mentioned the KMA July-October 2011-2014. The trained models were used to validate sentiments on Twitter 2015-2016, and the frequency of negative sentiments was compared with the satisfaction of forecast users. It was found that the frequency of the negative sentiments increased before satisfaction decreased sharply. And the tweet keywords and the news headlines were qualitatively compared to analyze the cause of negative sentiments. As a result, it was revealed that the individual caused the increase in the monthly negative sentiments increase in 2016. This study represents the value of sentiment analysis that can complement user satisfaction surveys. Also, combining Twitter and news headlines provided the idea of analyzing the causes of dissatisfaction that are difficult to identify with only satisfaction surveys. The results contribute to improving user satisfaction with weather services by efficiently managing changes in satisfaction.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.65-71
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• 2018

In the digital era, news consumption is not confined in geological boundaries. Technological advances bring the instant dissemination of news into life and allow news audience to consume events that occur far away almost in real time. The transmission has blurred the boundary between traditional media and new media, and the one between physical and virtual world. That is, what if a journalist applies news framing to the news translation process? This paper aims to investigate the gap between the ST and the TT created when the source news texts undergo a translation process. To achieve this aim, the appraisal theory developed by White (2003) is employed to identify a difference between the ST and the TT. Furthermore, we have attempted to identify differences between soft news stories and hard news stories while the STs from both news stories are translated into the TTs. Two time-sensitive events, Hugh Grant's marriage and a U.S. and North Korea summit, were selected. The former (a soft news story) is extracted from the Telegraph and the latter (a hard news story) is from the Washington post. As a result, it was found that such strategies as attitude, engagement, and judgment were used when the source news texts from the hard news story are translated into the target news texts. Under the appraisal theory, the strategies involve evaluative language which refers to positive or negative language that judges the worth of entities. In general, it is said that a journalist frames the SS (especially from the hard news story) to convey his ideology to news consumers. Hypothetically, we assume that a similar framing process takes place in deriving the TT from the SS of the hard news story. Thus, we could conclude that the TT from the hard news story differs from the TT from the soft news story and that the difference can be explained within the framework of White's appraisal theory.

• Journal of Korea Game Society
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• v.13 no.4
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• pp.61-72
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• 2013

This study explored the relationships between media use and attributions of negative ffects of video games on attitudes toward Shut-down system. To prevent the youth from game addiction and secure a right to sleep for the youth, Shut-down system ahs been enacted since the fall of 2011. Interview survey revealed that females, the aged, those who use more news products, those who perceive more negative effects of games, and those who attribute the negative effects of game on government would have more positive attitudes toward the shut-down system.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.249-258
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• 2019

Changes in media technolgy affect the competitive status of broadcasting networks as news media. The competitive media environment has pushed broadcasting network news programs to find new ways for leveling their qualitative performance up and rating. This study focuses on the empirical relationship between the two key value, news quality in terms of fairness and in-depthness and news ratings. This study is based on the analysis of broadcasting network news texts and individual news item raitngs. Empirical relationship between news quality factors and ratings was proved positive. But the relationship between the length of news item and rating was proved negative.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.105-112
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• 2020

The present study aims to explore news sharers' characteristic. Specifically, it aims to look at news sharers' demographic characteristics, old media news usage and new media news usage. Besides, it also explores factors affecting news sharing behavior. The study used the second data of Korea Press Foundation. Findings from surveys suggest that first, news sharers are younger and have higher education than not news sharers. Second, news sharers use less news through old media while more news through new media. Third, political orientation, portal, SNS and online video platform new usage, messenger news reliability have positive effects on news sharing, while age and portal news reliability have negative effects on it. Based on these findings, implication, limitations, and topics for future research are discussed.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.89-96
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• 2017

Recently, a number of researches actively focus on the contents and sensitivity of information distributed through SNS as smartphones and SNS gained its popularity. In this paper, we collected online news data about SW education, extracted words after morphological analysis, and analyzed emotions of collected news data by calculating sentimental score of each news datum. Also, the accuracy of the calculated sentimental score was examined. As a result, the number of news related to 'SW education' in the collection period was about 189 per month, and the average of sentimental score was 0.7, which signifies the news related to 'SW education' was emotionally positive. We were positive about the importance of SW education and the policy implementation, but there were negative views on the specific method for the realization. That is, a lack of SW education environment and its education method, a problem related to improvement of SW developers and improvement of their labor conditions, and increase of private education in coding were the factors for the negative viewers.

• Korean journal of communication and information
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• v.62
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• pp.159-178
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• 2013

The current study examined the effects of the voters' perception of television news coverage of election poll results on their political participation intention. 700 voters participated in a telephone interview three weeks before the 2012 Korean presidential election. A structural equation modeling with the nationally representative sample was performed. The findings indicate the respondents were more likely to evaluate television news coverage of election poll results negatively when the news coverage presented that the candidate they supported was behind in the race, and the negative evaluation was linked to a greater third-person perception. The third-person perception, in turn, had an indirect effect on political participation intention through negative emotional responses. The results imply that voters' political position influences their perception of the television news coverage of election poll results, and this perception can have indirect effects on political participation.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• Journal of Korea Port Economic Association
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• v.30 no.3
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• pp.1-14
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• 2014

This paper aims at measuring how new information is incorporated into volatility estimates. Various GARCH models are compared and estimated with daily BDI(Baltic Dry Index) data. While most researchers agree that volatility is predictable, they differ on how this volatility predictability should be modelled. This study, hence, introduces the asymmetric or leverage volatility models, in which good news and bad news have different predictability for future. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. From the Ljung-Box test statistic for twelfth-order serial correlation for the level we do not find any significant serial correlation in the unpredictable BDI. The coefficients of skewness and kurtosis both indicate that the unpredictable BDI has a distribution which is skewed to the left and significantly flat tailed. Furthermore, the Ljung-Box test statistic for twelfth-order serial correlations in the squares strongly suggests the presence of time-varying volatility. The sign bias test, the negative size bias test, and the positive size bias test strongly indicate that large positive(negative) BDI shocks cause more volatility than small ones. This paper, also, shows that three leverage models have problems in capturing the correct impact of news on volatility and that negative shocks do not cause higher volatility than positive shocks. Specifically, the GARCH model successfully reveals the shape of the news impact curve and is a useful approach to modeling conditional heteroscedasticity of daily BDI.