• Monthly Duck's Village
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• s.70
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• pp.50-51
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• 2009

The duck meat market has grown significantly in recent years and is likely to continue with genetic and husbandry advances, making duck increasingly competitive to other poultry and meat products. Duck meat currently represents less than 10% of total poultry meat production and is largely concentrated in China and southern Asia.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.9-16
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• 2020

The purpose of this study is to understand the public needs for AI education actively promoted and supported by the current government. In doing so, 11 metropolitan news articles and Twitter posts regarding AI education that have been posted from January 1, 2018 to December 31, 2019 were collected. Then, word frequency analysis using TF(Term Frequency) method and LDA(Latent Dirichlet Allocation) method of topic modeling analysis were conducted. The topics of the news articles turn out to be a macroscopic policy support such as 'training female manpower in the AI field' and 'curriculum reform of university and K-12', whereas the topics of twitter delineate more detailed social perception on future society, such as future competencies and pedagogical methods, including 'coexistence with intelligent robots', 'coding education', and 'humane education competence development'. The findings are expected to be used to suggest the implications for the composition and management of AI curriculum as well as the basic framework of human resources development in the future industry.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.45-52
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• 2019

The current study aims to investigate the relationship between tax avoidance and firm risk in an emerging market called Iran. The study population consists of 400 observations and 80 companies listed on the Tehran Stock Exchange (TSE) over a five-year period during 2012 and 2016. The statistical model used in this study is a multivariate regression model; besides, the statistical technique used to test the hypotheses proposed in this research is panel data. The results showed that low effective tax rate (tax avoidance) is more consistent than the higher effective tax rate. Moreover, there is no significant relationship between tax avoidance and future tax rate volatility. The findings also proved that lower effective tax rates are positively associated with future stock price volatility. This implies that since Iranian firms have many financial problems because of economic sanctions, they have a tendency to delay the disclosure of bad news about their firms. Needless to say, when a huge number of negative news reaches its peak, they immediately will enter the market and lead to a remarkable fluctuation in stock prices.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.457-466
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• 2020

The purpose of this study is to find out what the main agenda of social formation is and how it changes through the media by utilizing the news big data of COVID-19 which is spreading recently, and to suggest the direction of future reporting. In order to achieve the purpose of the research, 47,816 cases of news big data reported from December 31, 2019 to March 11, 2020 were divided into four periods based on the fourth stage of the crisis warning for infectious diseases, and a total of 20 topics were derived. Based on the results of the Topic Modeling analysis, this study proposed the following. First, it is necessary to refrain from provocative expressions such as "anxiety" and "fear" and use neutral and objective reporting terms. Second, more in-depth and contextual news production is required, breaking away from simple event news production. Third, it is necessary to prepare detailed crisis communication manuals for each situation related to infectious diseases. Fourth, we need reports that focus on citizens-led efforts to overcome the crisis. This research has the academic significance that it is the first paper to analyze news big data on COVID-19 using the Topic Modeling Analysis method, and the policy significance that can be used as the basis for developing national crisis communication policy.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.169-186
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• 2020

This study aims to examine the characteristics of news content and users' reactions in local broadcasting Youtube news' channel, and explore how the local media should response in the new online video environment. YouTube Open API sampled 3,950 news-related videos uploaded over a month on 31 YouTube news channels nationwide. The content analysis was performed on the basis of the analysis of individual videos, such as characteristics of each content and users' reactions. As a result, a few news channels have produced digital-only content, but the ratio has been very low, most were broadcast replay videos with titles and formats uploaded as they were. In some cases, it still operates as a comprehensive channel, which failed to show its expertise as an independent digital news platform. This shows that theses YouTube channels lacks differentiation from TV or its own web page, and is still skewed to the auxiliary role or online archive function of TV platform. Nevertheless, digital-only content, which can be a national issue based on regional expertise, has led to a higher number of views and users reactions, suggesting that is a realistic and effective strategy with expandability in online space in the future.

• The Journal of Industrial Distribution & Business
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• v.12 no.1
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• pp.47-62
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• 2021

Purpose: Our purpose in this study was to examine the strategies for the development of watermelon industry using unstructured big data analysis. That is, this study was to look the change of issues and consumer's perception about watermelon using big data and social network analysis and to investigate ways to strengthen the competitiveness of watermelon industry based on that. Methodology: For this purpose, the data was collected from Naver (blog, news) and Daum (blog, news) by TEXTOM 4.5 and the analysis period was set from 2015 to 2016 and from 2017-2018 and from 2019-2020 in order to understand change of issues and consumer's perception about watermelon or watermelon industry. For the data analysis, TEXTOM 4.5 was used to conduct key word frequency analysis, word cloud analysis and extraction of metrics data. UCINET 6.0 and NetDraw function of UCINET 6.0 were utilized to find the connection structure of words and to visualize the network relations, and to make a cluster of words. Results: The keywords related to the watermelon extracted such as 'the stalk end of a watermelon', 'E-mart', 'Haman', 'Gochang', and 'Lotte Mart' (news: 015-2016), 'apple watermelon', 'Haman', 'E-mart', 'Gochang', and' Mudeungsan watermelon' (news: 2017-2018), 'E-mart', 'apple watermelon', 'household', 'chobok', and 'donation' (news: 2019-2020), 'watermelon salad', 'taste', 'the heat', 'baby', and 'effect' (blog: 2015-2016), 'taste', 'watermelon juice', 'method', 'watermelon salad', and 'baby' (blog: 2017-2018), 'taste', 'effect', 'watermelon juice', 'method', and 'apple watermelon' (blog: 2019-2020) and the results from frequency and TF-IDF analysis presented. And in CONCOR analysis, appeared as four types, respectively. Conclusions: Based on the results, the authors discussed the strategies and policies for boosting the watermelon industry and limitations of this study and future research directions. The results of this study will help prioritize strategies and policies for boosting the consumption of the watermelon and contribute to improving the competitiveness of watermelon industry in Korea. Also, it is expected that this study will be used as a very important basis for agricultural big data studies to be conducted in the future and this study will offer watermelon producers and policy-makers practical points helpful in crafting tailor-made marketing strategies.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.6
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• pp.871-879
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• 2010

In this paper, we present a system that classifies comments on a news article into a user opinion called a polarity (positive or negative). The system is a kind of document classification system for comments and is based on machine learning techniques like support vector machine. Unlike normal documents, comments have their body that can influence classifying their opinions as polarities. In this paper, we propose a feature weighting scheme using such characteristics of comments and several resources for opinion classification. Through our experiments, the weighting scheme have turned out to be useful for opinion classification in comments on Korean news articles. Also Korean character n-grams (bigram or trigram) have been revealed to be helpful for opinion classification in comments including lots of Internet words or typos. In the future, we will apply this scheme to opinion analysis of comments of product reviews as well as news articles.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.145-159
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• 2020

Because of the recent development of information and communication technology, companies are exposed to various media such as blogs, social media, and YouTube. In particular, exposed news affects the company's reputation. So, while positive news can improve corporate value, negative news can lead to financial losses for the company. In this study, we redefine corporate reputation as social responsibility, vision and leadership, financial performance, products and services through existing literature, and conducted an AHP survey with a total of four components to calculate the weight of each factor. As a result of the calculation, the proportion of financial performance was the highest at 0.41, and products and services, vision and leadership, and social responsibility were the lowest. In addition, in order to measure the reputation of a company, it is classified as a component that defines online news using the LDA technique. In addition, through sentiment analysis, an index for each corporate reputation factor was derived, and the reputation index was calculated by combining it with the AHP analysis result, and Spearman ranking correlation analysis was performed to secure the validity of the research results. Therefore, the significance of this study is that the definition and importance of the constituent factors can contribute to the future planning and development direction of the company, and also contribute to the derivation of the corporate reputation index. This study is significant in that a new analysis methodology that applied AHP analysis results to sentiment analysis was suggested.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.118-141
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• 2019

In South Korea, as awareness of gender equality increased since the 1990s, policies for gender equality and social awareness of equality have been established. Until recently, however, the gap between men and women in social and economic activities has not reached the globally desired level and led to social conflict throughout the country. In this study, we analyze the content of online news comments to understand the public perception of gender equality and the details of gender conflict and to grasp the emergence and diffusion process of emerging issues on gender equality. We collected text data from the online news that included the word 'gender equality' posted from January 2012 to June 2017 and also collected comments on each selected news item. Through text mining and the temporal semantic network analysis, we tracked the changes in discourse on gender equality and conflict. Results revealed that gender conflicts are increasing in the online media, and the focus of conflict is shifting from 'position and role inequality' to 'opportunity inequality'.