• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.779-803
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• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.196-204
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• 2002

This paper examines the price discovery role of the KOSPI 200 futures index for its cash index. It was used the intrady data for KOSPI 200 and futures index from July 1998 to June 2001. The existing Preceding study for KOSPI 200 futures index was used the data of early market installation, but this study is distinguished to use a recent data accompanied with the great volume of transaction and various investors. We established three hypothesis to examine whether there is the price discovery role in the KOPSI 200 futures index and the characteristics of that. First, to examine whether the lead-lag relation is induced by the infrequent trading of component stocks, observations are sorted by the size of the trading volume of cash index. In a low trading volume, the long lead time is reported and the short lead time in a high volume. It is explained that the infrequent trading effect have an influence on the price discovery role. Second, to examine whether the lead-lag relation is different under bad news and good news, observations are sorted by the sign and size of cash index returns. In a bad news the long lead time is reported and the short lead time in a good news. This is explained by the restriction of"short selling" of the cash index Third, we compared estimates of the lead and lag relationships on the expiration day with those on days prior to expiration using a minute-to-minute data. The futures-to-spot lead time on the expiration day was at least as long as other days Prior to expiration, suggesting that "expiration day effects" did not demonstrate a temporal character substantially different form earlier days. Thus, while arbitrage activity may be presumed to be the greatest at expiration, such arbitrage transactions were not sufficiently strong or Pervasive to alter the empirical price relationship for the entire day. for the entire day.

• Informatization Policy
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• v.26 no.4
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• pp.20-35
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• 2019

We empirically analyze the effects of psychological factors, such as the fear of parenting, on fertility rates. An index is calculated based on the share of negative news articles on child care in all social articles from 2000 to 2018. The analysis result shows that as the index increases, the fertility rate after three years falls. This result is repeated in the correlation analysis, simple regression, and VAR analysis. According to Granger causality analysis, it is found that the relation between the index and the fertility rate after three years is not just a simple correlation but a causal relationship. There are differences among age groups. The fertility rate of women in their 20s and 30s shows a significant response to the index, but that of the 40s does not. The index affects the birthrate of first child, but do not affect the birthrate of second or more children. These results are consistent with the intuition that younger women are more likely to be affected by the negative articles about parenting, but not to those who have already experienced childbirth. This study is meaningful in that a significant index for predicting social phenomena is extracted beyond the limited use of news big data such as a simple keyword mention volume monitoring. Also, this big data-based index is a 3-year leading indicator for fertility, which provides the advantage of providing information that helps early detection.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.33-46
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• 2021

Using news big data analysis, this study was aimed to examine the suicide phenomena in Korean society, and to evaluate whether suicide prevention policies reflect social phenomena appropriately. For this purpose, 9,142 news titles with suicide as the keyword were collected from eight central newspapers between 2000 to 2018. Nouns were extracted, and data was refined for network analysis. The total period was divided into 4 periods based on the 1st and 2nd suicide prevention policies, and the characteristics of suicide phenomena in each period were identified through the top 50 frequent main words and the clusters. As a result, period 1 (2000~2003) with 6 clusters (military, internet environment, economic problems, pessimism, school, corruption), period 2 (2004~2008) with 8 clusters (high social class, school, economic problems, suicide attempts, family issues, social problems, military, responsibilities), period 3 (2009~2013) with 6 clusters (school, family issues, suicide attempts, occupation, military, investigation), and period 4 (2014~2018) with 8 clusters (military, suicide insurance money, family issues, suicide attempts, occupation, job stress, celebrity, corruption) were identified. Study results suggested the characteristics of suicide phenomena in our society. Further, the appropriateness of the implementation of suicide prevention policies was discussed.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.51-58
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• 2022

Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• Journal of Sport and Applied Science
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• v.8 no.1
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• pp.11-17
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• 2024

Purpose: The purpose of the study was to understand a trend of esports in terms of gamers' and fans' perceptions toward esports using social big data. Research design, data, and methodology: In this study, researchers first selected keywords related to esports. Then a total of 10,138 buzz data created at twitter, Facebook, news media, blogs, café and community between November 10, 2022 and November 19, 2023 were collected and analyzed with 'Textom', a big data solution. Results: The results of this study were as follows. Firstly, the news data's main articles were about competitions hosted by local governments and policies to revitalize the gaming industry. Secondly, As a result of esports analysis using Textom, there was a lot of interest in the adoption of the Hangzhou Asian Games as an official event and various esports competitions. As a result of the sentiment analysis, the positive content was related to the development potential of the esports industry, and the negative content was a discussion about the fundamental problem of whether esports is truly a sport. Thirdly, As a result of analyzing social big data on esports and the Olympics, there was hope that it would be adopted as an official event in the Olympics due to its adoption as an official event in the Hangzhou Asian Games. Conclusions: There was a positive opinion that the adoption of esports as an official Olympic event had positive content that could improve the quality of the game, and a negative opinion that games with actions that violate the Olympic spirit, such as murder and assault, should not be adopted as an official Olympic event. Further implications were discussed.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.365-374
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• 2018

This study aims at understanding the components of on-line newspaper and how each newspaper's layout configuration is differentiated through the analysis on the websites of 10 general daily newspapers in the news stand of NAVER. The collection of data was implemented twice, and One-Way ANOVA was used as an analyzing way. The content of the analysis was carried out based on the types of visual images, the number of photo-based articles and title-based articles, the size of image for the main story, etc. As a result of analysis, the rate of news articles with the audios, videos, cards and slides differentiated from paper-based newspaper was low, and also the news articles using the informative graphics and the graphic sources were very small in number. As a whole, the newspapers in the news stand of NAVER showed that they attempt to make a distinction of their newspaper layout by using a variety of editorial techniques. The significance of this paper is to offer a basic clue to the editing formation to promote the news consumption of newspapers. Under the circumstance that the ecology of media is rapidly being reformed by new media technology, the continuous study of how the newspaper layout should be changed will be needed.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.391-406
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• 2015

As the individual participation in online news sites proliferates, the importance of online news comments has been increasing. Social comment services which help people leave comments on news articles using their own SNS (social networking site) accounts have gained popularity recently. Using data gathered from an online news site, this study, therefore, (1) identifies factors differentiating social comments from general comments, (2) examines how social comments are significantly different from general comments in terms of each factor, (3) and further validates how the social comments' characteristics vary among different type of SNS. Then, we investigated this study by applying t-test, ANOVA, and Duncan test of SPSS Statistics. Our results provide insights on the significant differences in all the factors between general and social comments. We also found that there is a significant difference between Facebook and Twitter groups among three types of SNS. The findings of this study would help assess the actual benefit of social comment services as they may provide us with several valuable leads to solve the malicious comments issue. Moreover, they would suggest the need to apply this service to other areas, such as online environments in private and public sectors.