• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.19-39
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• 2015

Internet of Things (IoT) is expected to provide efficiency and convenience in human life by integrating the Internet into the things that we use in daily lives. IoT can not only create new businesses but also can bring great changes in our lives thanks to the various ways of technical application: defining relationships among things or automatic use of technology by analyzing the usage pattern. This study uses the qualitative research of interviewing the experts to predict the changes that IoT technology is expected to bring in our lives. In addition, this paper analyzes news articles about internet of things in Korea using text-network analysis. This study also discusses the factors which need to be considered to put IoT into successful use in business contexts.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.515-526
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• 2020

Digital healthcare is a combined area of medical field and IT and various information on digital healthcare is provided in social media. This study aims to find the research trend of digital healthcare by collecting and analyzing data related to digital healthcare through the social media. The data were collected from Naver and Daum's news and blogs from January 2008 to June 2019. Major keywords with high frequency were extracted and visualized with wordcloud and network analysis was used to analyze the relationship between major keywords. Research combining medical field and IT from 2008 to 2001, various convergence research based on medical field and IT from 2012 to 2015, convergence research that applied the 4th industrial revolution technologies such as big data, blockchain and AI were actively conducted from 2016 to June 2019.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.460-476
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• 2015

The purpose of this study is to provide practical implications on government crisis management strategies and on the use of SNS in crisis management. Specifically, this study analyzed Ministry of Drug and Food Safety's responses to paraben toothpaste issue, media coverage of paraben toothpaste issue, and public responses to paraben toothpaste issue. Through textual analysis and the network analysis of 45 news articles and 645 tweets, this study found that Ministry used one-way communication strategy and mostly negative issues regarding Ministry's crisis response strategies were diffused via the media and Twitter. This study was meaningful in that it highlighted the importance of media relations and use of SNS in crisis management. The findings of this study provide useful implications for government officials and PR practitioners in their crisis management and communication strategy.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.533-547
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• 2018

The objective of this study is to examine the perception of golfwear and related trends based on major keywords and associated words related to golfwear utilizing big data. For this study, the data was collected from blogs, Jisikin and Tips, news articles, and web $caf{\acute{e}}$ from two of the most commonly used search engines (Naver & Daum) containing the keywords, 'Golfwear' and 'Golf clothes'. For data collection, frequency and matrix data were extracted through Textom, from January 1, 2016 to December 31, 2017. From the matrix created by Textom, Degree centrality, Closeness centrality, Betweenness centrality, and Eigenvector centrality were calculated and analyzed by utilizing Netminer 4.0. As a result of analysis, it was found that the keyword 'brand' showed the highest rank in web visibility followed by 'woman', 'size', 'man', 'fashion', 'sports', 'price', 'store', 'discount', 'equipment' in the top 10 frequency rankings. For centrality calculations, only the top 30 keywords were included because the density was extremely high due to high frequency of the co-occurring keywords. The results of centrality calculations showed that the keywords on top of the rankings were similar to the frequency of the raw data. When the frequency was adjusted by subtracting 100 and 500 words, it showed different results as the low-ranking keywords such as J. Lindberg in the frequency analysis ranked high along with changes in the rankings of all centrality calculations. Such findings of this study will provide basis for marketing strategies and ways to increase awareness and web visibility for Golfwear brands.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.315-334
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• 2013

This study performs opinion mining of newspaper articles, based on topics extracted by topic modeling. We analyze the attitudes of the news media towards a major issue of 'presidential election', assuming that newspaper partisanship is a kind of opinion. We first extract topics from a large collection of newspaper texts, and examine how the topics are distributed over the entire dataset. The structure and content of each topic are then investigated by means of network analysis. Finally we track down the chronological distribution of the topics in each of the newspapers through time serial analysis. The result reveals that both the liberal newspapers and the conservative newspapers exhibit their own tendency to report in line with their adopted ideology. This confirms that we can count on opinion mining technique based on topics in order to analyze opinion in a reliable fashion.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.138-147
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• 2018

The network analysis is done to explore what kind if issues are diffused about earthquake and the role of social media. The types of disaster information is classified into formal and informal. The role of actor is classified based on the concentrativeness and the diffusion of issue. Youttube is functioned as a formal channel and an informal channel when disaster happened. In case of government's video, issue contractiveness is high but the diffusion is low. In case of media's video, issue contractiveness and the diffusion are all high. In case of individual channel, issue contractiveness is low, but diffusion is high. In disaster, youtube is a tool to respread the disaster issue. Government needs to try diffusion of government's news actively in disaster.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.211-226
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• 2016

Most of the SNS users argue that they feel techno-stress or digital fatigue when they use SNS. As the relationships in the SNS expand, users may feel work overload, digital fatigue, and techno-stress which are caused by the time and effort for the retaining the existing relationships established via SNS. The SNS activities require users' time and effort to update their profiles and the current news of them, responding to online friends' contents. Thus, more relationships they have, more stress they can feel. This study tries to examine the key factors that can affect subjective well-being of individuals in Social Network Service (SNS) usage. Therefore, this study, based on the previous literature, investigates what the sources of SNS stress are and how SNS stress and flow affect subjective well-being of SNS users. Major findings of this study from an empirical analysis with 201 SNS user respondents who have accessed SNS at least one time within one month are as follows. First, perceived opportunity cost and reputation recognition in SNS usage were found to have significant effects on negative emotion. Second, individual's flow in SNS was significantly affected by challenges and interactions, and had a significant impact on positive emotion. However, SNS users' flow did not show a positive relationship with their satisfaction of life. This study contributes to the expansion of theoretical discussion about the effect of individual's SNS usage on quality of life in validating whether SNS usage can bring individuals subjective well-being. Implications of the study findings and future research directions are also discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.924-929
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• 2007

Captions which appear in images include information that relates to the images. In order to obtain the information carried by captions, the methods for text extraction from images have been developed. However, most existing methods can be applied to captions with fixed height of stroke's width. We propose a method which can be applied to various caption size. Our method is based on connected components. And then the edge pixels are detected and grouped into connected components. We analyze the properties of connected components and build a neural network which discriminates connected components which include captions from ones which do not. Experimental data is collected from broadcast programs such as news, documentaries, and show programs which include various height caption. Experimental result is evaluated by two criteria : recall and precision. Recall is the ratio of the identified captions in all the captions in images and the precision is the ratio of the captions in the objects identified as captions. The experiment shows that the proposed method can efficiently extract captions various in size.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.271-280
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• 2012

Currently, contents generated by SNS services are increasing exponentially, as the number of SNS users increase. The SNS is commonly used to post personal status and individual interests. Also, the SNS is applied in socialization, entertainment, product marketing, news sharing, and single person journalism. As SNS services became available on smart phones, the users of SNS services can generate and spread the social issues and controversies faster than the traditional media. The existing indexing methods for web contents have limitation in terms of real-time indexing for SNS contents, as they usually focus on diversity and accuracy of indexing. To overcome this problem, there are real-time indexing techniques based on RDBMSs. However, these techniques suffer from complex indexing procedures and reduced indexing targets. In this regard, we introduce the TK-Indexing method to improve the previous indexing techniques. Our method indexes the generation time of SNS contents and keywords by way of NoSQL to indexing SNS contents in real-time.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.83-92
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• 2018

Many studies in deep learning show results as good as human's decision in various fields. And importance of activation of online-community and SNS grows up in game industry. Even it decides whether a game can be successful or not. The purpose of this study is to construct a system which can read texts and create comments according to schedule in online-community and SNS using deep learning. Using recurrent neural network, we constructed models generating a comment and a schedule of writing comments, and made program choosing a news title and uploading the comment at twitter in calculated time automatically. This study can be applied to activating an online game community, a Q&A service, etc.