• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.436-447
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• 2021

The boycott of Japanese products triggered by Japan's economic retaliation has heated up the Republic of Korea. This study examined the factors affecting the boycott participation intention in 217 college students and ordinary people in their 20s and 30s. The results of the study showed that perceived egregiousness, self-efficacy, and subjective norm had a positive effect on boycott participation intention, and perceived egregiousness had an indirect effect on boycott participation intention through anger. In addition, these overall impacts were moderated by online and SNS news usage. This study is significant in providing academic and practical implications for understanding boycott phenomena by verifying various influencing factors on consumer boycott intentions and comprehensively reviewing the mediating effect of anger and the moderating effect of online and SNS news usage.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.1-22
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• 2016

News contents has been collected, selected, edited and sometimes distorted by the news recommendation mechanisms of online portals in nowadays. Prior studies had not confirmed the consensus of newsworthiness, and they had not tried to empirically validate the impacts of newsworthiness on audience reactions. This study challenged to summarize the concepts of newsworthiness and validate the impact of representativeness of both editor's and audience's perspective on audience reactions as perceived news quality, trust on news portal, perceived usefulness, service satisfaction, loyalty, continuous usage intention, and word-of-mouth intention by adopting the representativeness heuristics method and information adoption model. 357 valid data had been collected using a scenario survey method. Subjects in each groups are exposed by 3 news recommendation mechanisms: 1) the time-priority news exposure mechanism (control group), 2) the reference-score-based news recommendation mechanism (a single treatment group), and 3) the major-news-priority exposure mechanism sorting by the reference scores made by peer audiences (the mixed treatment group). Data had been analyzed by the MANOVA and PLS method. MANOVA results indicate that only mixed method of both editor and audience recommendation mechanisms impacts on perceived news quality and trust. PLS results indicate that perceived news quality and trust could significantly affect on the perceived usefulness, service satisfaction, loyalty, continuance usage, and word-of-mouth intention. This study would contributions to empathize the role of information technology in media industry, to conceptualize the news value in the balanced views of both editors and audiences, and to empirically validate the benefits of news recommendation mechanisms in academy. For practice, the results of this study suggest that online news portals would be better to make mixed news recommendation mechanisms to attract audiences.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.25-31
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• 2018

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.177-185
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• 2021

This paper aimed to grasp what the percentage in the types of news stories on the online broadcast is, which was conducted by analyzing the news stories of 9 broadcasting websites on the Naver newsstand. For the analysis, a total of 270 days' samples were selected, including 30 days per broadcast on 9 broadcasting websites. For a method of analysis, One-way ANOVA was used to examine the difference among broadcasting websites. The analysis was made centering with priorities given to the type of news stories by the composition of language, the type of genre as a standard of stories, and so on. As a result of analysis, all the programs in the off-line broadcast have been produced and transmitted as a video-typed story, but a half of those in on-line broadcast have been made up of the stories composed of photo and text. The online newspaper has been producing a new type of news' story using video-typed story or computer graphic while the online broadcast has actively been utilizing stories composed of photos and text, which are types of newspaper's stories. From above-mentioned results, it can be understood that the boundary among media is getting more and more indistinct on the environment of online media, showing the phenomenon that the type of broadcast's stories is becoming old-fashioned.

• International Journal of Contents
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• v.12 no.3
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• pp.1-11
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• 2016

This exploratory study examines the critical factors associated with news consumers' perception of information overload and news consumption patterns. An online survey was conducted with Qualtrics panels (N = 1001). The demographics and three antecedent factors of perceived information overload were considered including the frequency of news access through multiple media platforms, level of attention to news, and interest in news. Three news consumption patterns were investigated as possible consequences of perceived information overload: news avoidance, selective exposure, and willingness to pay for news. The results of hierarchical regression analyses revealed a meaningful distinction between general and news information overload. Overall, news consumers who paid more attention to news through newer media/platforms/devices perceived higher levels of information overload, were more willing to pay for the news, and often avoided news or selectively exposed themselves to certain sources of news to manage news information overload.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.150-161
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• 2019

This study analyzed the impact of social trust and conflict perception of news users on news use. To this end, 548 adults aged 20 and under 69 were surveyed online. The analysis results are as follows. First, the level of awareness of social conflict has been shown according to people's political orientation. Second, the higher the trust in society, the greater the use of news regardless of land-based, comprehensive, and online channels. Third, the perception of social conflict was related to the use of news through JTBC, TV Chosun, Channel A and YouTube. Fourth, the age and political orientation of news users influenced the use of news by channel. Finally, the more progressive the tendency was to use news through JTBC or to watch news on portals. On the other hand, the more progressive the use of news through three terrestrial broadcasters, TV Chosun, and Channel A decreased. In conclusion, this study is meaningful in terms of the user-oriented discussion of the news environment and the impact of an individual's social perception on news use.

• Proceedings of the CALSEC Conference
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• 1999.07a
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• pp.33-40
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• 1999

ㆍ No Conflict with Existing Investments in Physical stores ㆍ "First-Mover" Advantages: Early Lead in Market Share and Brand Recognition ㆍ Technology Infrastructure ㆍ "Web-Savvy" Management(omitted)

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.575-582
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• 2020

We proposes a method to estimate political preference of users who write comments on internet news. We collected and analyzed a massive amount of new comment data from internet news to extract features that effectively characterizes political preference of users. We expect that it helps user to obtain unbiased information from internet news and online discussion by providing estimated political stance of news comment writer. Through comprehensive tests we prove the effectiveness of two proposed methods, lexicon-based algorithm and similarity-based algorithm.

• Korean journal of communication and information
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• v.30
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• pp.143-174
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• 2005

This study aims at applying Chaffee & McLeod's co-orientation model to Internet portal news providers and users to find out their mutual understanding toward various features of online news. Included in those features are interactivity, expansion of user role, larger choices(user characteristics), real time update of news, limitless quantity, contextualized contents through hypertext, data base service, and multimedia contents(contents characteristics). To test the level of agreement, accuracy and congruency between the parties, a survey was conducted among 105 portal news providers from 11 online news media, and 105 portal news users between ages 20 and 40. The result indicated that both portal news providers and users showed agreement for user characteristics, but by and large displayed either ignorance or partial congruency toward contents characteristics. Communication between portal news providers and users are thus exported to increase until it reaches the point where the internet's newly born identity as a news medium gets finally stabilized.