• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.127-137
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• 2021

Mainland China area has been a long-term, major trade rival and partner of Taiwan, accounting for more than 40% of Taiwan's total annual trade exports, and so Sino-US trade friction is expected to have a significant impact on Taiwan's economy in the future. This study focuses on major bad news of Sino-US trade frictions and how it generates short-term shocks for Taiwan's equity market and fear sentiment. It further explores the mutual interpretation relationship between price changes such as VIX, Taiwan's stock market index, and the VIX ETF to identify which factors have information leadership as leading indicators. The study period covers 750 trading days from 2017/1/3 to 2020/1/31. This study finds that, when a policy news is announced, the stock market index falls significantly, the change in the trading price (net value) of the VIX ETF rises significantly, and the overprice rate significantly drops, but VIX does not, showing that fear sentiment exists in the Taiwan's market. The net value of the VIX ETF shows an information advantage as a leading indicator. This study suggests that, when the world's two largest economies clash over trade, the impact on Taiwan's equity market is inevitable, and that short-term fear effects will arise.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.2
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• pp.43-66
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• 2016

The primary objective of this study was to investigate the perception of informational professionals' knowledge sharing practices in social media platforms. The specific objectives of the study included learning professionals' perceptions and awareness of knowledge sharing using social media, understanding their opinions and beliefs, and gaining familiarity with and reasons for using these tools. Open & close ended web-based questions were sent out by email to the international training program (ITP) participants. Findings indicated that most of the respondents' were aware of using social media and that they used social media for knowledge sharing. Speed and ease of use, managing personal knowledge, easier communication with users and colleagues and powerful communication tool are the areas that motivated them to use it. It also stated some barriers like lack of support, familiarity, trust, unfiltered information and fear of providing information. The study was limited to the perceptual aspect of the issue, specifically from the individuals' opinions and sentiments.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.7
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• pp.984-999
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• 2013

This research examines the interrelation of clothes, colors and sentiments based on clothes and colors that stimulate sentiment. This study provides data that is useful to color therapy by means of clothes as medium. The survey for this study targeted 200 Pusan National University students who analyzed the colors of association and clothes colors for nine positive vocabularies (passion, love, warmth, happiness, interest, softness, comfortable, freshness, and coolness) and six negative vocabularies (anger, fear, despair, nervous, gloomy, and loneliness). The data collection process used 120 standard colors as represented by Munsell's basic 10 colors (R, YR, Y, GY, G, BG, B, PB, P, RP) as chromatic colors classified into eleven tones of colors (V, S, B, P, VP, LGR, GR, L, DL, DP, DK) and achromatic colors divided into ten steps of brightness N1-N10. The results of the research are as follows. First, the warm class of colors were significant in the colors of association with positive sentiment and the cold class of colors were significant in the sentiment of refreshment and coolness. In addition, bright and clear colors (like V, S, VP, P) were associated with color tones. Second, the low bright achromatic colors were generally high for the colors of association with negative sentiment; in addition, the color of R, PB, P (as achromatic colors) were also significant. In addition, sober and dark tones (like GR, DL, DK, DP) were significant in color tones. Third, the interrelation between positive sentiment and clothes colors shows that colors of association were mainly used for upper garment colors. Similar colors against upper garments were used together for bottom garment achromatic colors and complementary colors; therefore, bottom garments play a subsidiary role in the concept of coordination with upper garments.

• Journal of Science and Technology Studies
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• v.8 no.2
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• pp.27-56
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• 2008

The controversy over the mad cow disease in Korea can be understood as a demonstration of complex and skeptical public sentiments toward the government that were all mixed with expectations, demands, hope and mistrust. The governments decision to resume the import of American beef turned such expectations into mistrust and public hope into nationwide resistance expressed in the form of candlelight vigils. This phenomenon can not be simply explained as a fear of risks. Concerns over mad cow disease were a trigger but they were riot sufficient cause to explain the nationwide controversy involving all the Koreans. It was mad cow disease that triggered a sharp confrontation between the government trying to stick to its decision to resume the import of American beef and the opponents who were not convinced by the government. In fact, this is not simply an issue of a disease. It is rather a complex issue of the acceptance of risks, the consistency of the government policy, trust in the government, and the public consensus on the government policy. In this context, this study analyzes the controversy over the mad cow disease from the perspectives of risk management, public policy-making, and public trust and social consensus-building.

• Asian Journal for Public Opinion Research
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• v.12 no.1
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• pp.53-74
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• 2024

The proliferation of health misinformation gained momentum amidst the outbreak of the novel coronavirus disease 2019 (COVID-19). People stuck in their homes, without work pressure, regardless of health concerns towards personal, family, or peer groups, consistently demanded information. People became engaged with misinformation while attempting to find health information content. This study used the content analysis method and analyzed 1,154 misinformation stories from four prominent signatories of the International Fact-Checking Network during the pandemic. The study finds the five main categories of misinformation related to the COVID-19 pandemic. These are 1) the severity of the virus, 2) cure, prevention, and treatment, 3) myths and rumors about vaccines, 4) health authorities' guidelines, and 5) personal and social impacts. Various sub-categories supported the content characteristics of these categories. The study also analyzed the emotional valence of health misinformation. It was found that misinformation containing negative sentiments got higher engagement during the pandemic. Positive and neutral sentiment misinformation has less reach. Surprise, fear, and anger/aggressive emotions highly affected people during the pandemic; in general, people and social media users warning people to safeguard themselves from COVID-19 and creating a confusing state were found as the primary motivation behind the propagation of misinformation. The present study offers valuable perspectives on the mechanisms underlying the spread of health-related misinformation amidst the COVID-19 outbreak. It highlights the significance of discerning the accuracy of information and the feelings it conveys in minimizing the adverse effects on the well-being of public health.

• (The) Research of the performance art and culture
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• no.32
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• pp.595-628
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• 2016

Pansori of a creative group pansori 2006 demonstration factory floor sound brutal sorigeuk the home of is a legend 'deokttaegol' in pansori, a creative for adaptation to remakes Work is. Evil Twin 'deokttaegol' called "Give me my leg back" in of Ghost Stories, broadcast on a kbs of lines from breakneck work is considered to be a pronoun. Sound and shadow play and playing drums and payments sentiments of the cruelty I've come across in this 'Give me my leg back' audience to be deployed to the cruel is formed by the center. Based on emotional horror of cruelty. When I was little, ever heard of Korean Ghost Stories, a bedrock of the main feeling revulsion of value in a short time and is contained in a story of filial piety, while in education, to the target Provided. Done in our lives using genre called 'pansori' sentiment and efficient learning can move about the value education can know. Sound and stories, many carefree a stimulus such as Pansori is a great gesture can be a means of education. Valued with any information, work is performed in pansori, depending upon efficient and the various, education and made an emotional cultivation resulting from the value. In my life friendly, our own via a variety of materials that can easily access many values and sentiments, and to culture for each age group on languages and customs Each age groups and instructive preferred allowing them access through their rhythm, pansori, access to the target is persistent about it with curiosity and interest. Can have interest. This wealth not belong to the traditional pansori and new together private and to the tune called creative work for the Pansori. Therefore, our language and customs, their poems span a friendly, the pansori and created using the vocabulary for each age group creative content is educational effects if used in education It is expected to be big thing. These effective approach for each age group and based on the vocabulary by the content easily understood lessons by causing only a smoothly acquired Can to provide an opportunity. Therefore, the Pansori of a creative education is important to take advantage of educational value.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.