• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.13-31
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• 2016

Despite expectations of short- or long-term positive effects of corporate social responsibility (CSR) on firm performance, the results of existing research into this relationship are inconsistent partly due to lack of clarity about subordinate CSR concepts. In this study, keywords related to CSR concepts are extracted from atypical sources, such as newspapers, using text mining techniques to examine the relationship between CSR and firm performance. The analysis is based on data from the New York Times, a major news publication, and Google Scholar. We used text analytics to process unstructured data collected from open online documents to explore the effects of CSR on short- and long-term firm performance. The results suggest that the CSR index computed using the proposed text - online media - analytics predicts long-term performance very well compared to short-term performance in the absence of any internal firm reports or CSR institute reports. Our study demonstrates the text analytics are useful for evaluating CSR performance with respect to convenience and cost effectiveness.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.10-27
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• 2018

The purpose of this study is to explore the meaning of happiness and unhappiness in Korean society through text mining analysis. Similar words with keywords(happiness/unhappiness) from online news portal are extracted using Word2Vec and TF-IDF method. We also use the K-LIWC dictionary to perform the sentiment analysis of words associated with happiness and unhappiness. In TF-IDF analysis, happiness and unhappiness are highly related to social factors and social issues of the year. In Word2Vec analysis, 'Hope' has been similar with happiness for six years. In K-LIWC analysis, 'money/financial issues', 'school', 'communication' is highly related with happiness and unhappiness. In addition, 'physical condition and symptom' is highly related to unhappiness. Implications, limitations, and suggestions for future research are also discussed.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.2
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• pp.11-20
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• 2020

The goal of this study was to investigate the implementation and related issues of the school space innovation issued by key Korean mass media using text mining. To accomplish this goal, this study collected 519 news articles associated with the school space innovation issued by 54 Korean mass media companies. Based on this data, this study performed the frequency analysis and network analysis regarding the keywords. Based on the findings, the characteristics of school space innovation are summarized as follows: First, school space innovation has progressed in response to future education. Second, users are actively participating in school space innovation. Third, experts are supporting the innovation of school space by establishing a cooperative system. Fourth, the community is actively considering the innovation of school space. Fifth, the main projects of the Ministry of Education and the Provincial Offices of Education are actively conducted in a mix of top-down and bottom-up approaches. The findings of this study will contribute to providing a clear direction for contemporary school space innovation and implications for future research agenda and implementation.

• Journal of Korean Academy of Nursing
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• v.47 no.6
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• pp.806-816
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• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.195-200
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• 2019

The purpose of this paper is to explore security guard's status and roles in society through media reports. Research method is to anlyze security Guard's 'Keyword Trend' and 'Keyword Frequency Analysis' by 'Big Kind' which enables 'News Big Data' analysis. The result came out by the analysis in sectional private security guard's history of settling down, growing up (quantity), and growing up (quality) by separating generations is that there are lots of attention and exposure from media about crime, security guard job, minimum wage, and 'Gabjil', but the images of security guard are recognized as victim of crime and 'Gabjil', and working in poor environment with minimum waged and ambiguous job, instead of people preventing crimes. In the future, stabilizing security guard's social status and work responsibility, and developing job professionalism are necessary to improve the images of security guard.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.2
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• pp.85-101
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• 2021

This study aims to gain insight through understanding the Metaverse, which has recently become a hot topic. The study utilizes the methods of case study and News Bigdata Analysis Services. The Metaverse can be defined as a world with no separation between the virtual and real worlds. Currently, the Metaverse is dominated mainly by the MZ generation, but just like smartphones have quickly entered our lives, the Metaverse will soon, too, become a part of our lives. To follow up on this change, all companies, including global companies, are going after the Metaverse. Today, the Metaverse is successfully being used in all types of fields, including gaming, performing arts, business, etc., and its essential technologies include VR/AR/MR/XR and AI. This study intends to help understand the Metaverse through a case analysis of Zepeto, which has 200 million users worldwide. On Zepeto, users can decorate their own avatars, hang out with friends, go to art galleries and performances, and create and sell items. Of these users, 90% are from outside of South Korea, and 80% are teenagers. With most of the users being underage, many legal and social problems also follow. Nevertheless, who will be the first to conquer the new world of the Metaverse will continue to be a big issue. This study also analyzes domestic news articles about the Metaverse by utilizing the BigKinds system. Starting in 1996, the number of articles about the Metaverse each year remains single digit, until in 2020 when the number sharply rises to 86 news. As of June 2021, there are 1,663 articles on the Metaverse. This study suggests that the Metaverse should now be carefully examined and closely followed.

• 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.

• The Journal of Information Systems
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• v.31 no.1
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• pp.175-195
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• 2022

Purpose The purpose of this study is to examine news articles and analyze trends and key agendas related to artificial intelligence(AI). In particular, this study tried to compare the reporting behaviors of Korea and the United States, which is considered to be a leader in the field of AI. Design/methodology/approach This study analyzed news articles using a big data method. Specifically, main agendas of the two countries were derived and compared through the keyword frequency analysis, topic modeling, and language network analysis. Findings As a result of the keyword analysis, the introduction of AI and related services were reported importantly in Korea. In the US, the war of hegemony led by giant IT companies were widely covered in the media. The main topics in Korean media were 'Strategy in the 4th Industrial Revolution Era', 'Building a Digital Platform', 'Cultivating Future human resources', 'Building AI applications', 'Introduction of Chatbot Services', 'Launching AI Speaker', and 'Alphago Match'. The main topics of US media coverage were 'The Bright and Dark Sides of Future Technology', 'The War of Technology Hegemony', 'The Future of Mobility', 'AI and Daily Life', 'Social Media and Fake News', and 'The Emergence of Robots and the Future of Jobs'. The keywords with high centrality in Korea were 'release', 'service', 'base', 'robot', 'era', and 'Baduk or Go'. In the US, they were 'Google', 'Amazon', 'Facebook', 'China', 'Car', and 'Robot'.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.113-119
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• 2023

Despite the lifting of the mandatory wearing of masks, which was the main tool of the COVID-19 quarantine policy, we paid attention to the fact that some people are still wearing masks, and we wanted to clarify why people do not take off their masks. Through a survey in this regard, we were able to ascertain why some people continue to wear masks in a broader context. In this article, we directly and indirectly confirm the hidden side of citizens' continued wearing of masks by analyzing how the lifting of the mask-wearing obligation was reported in media articles that have a significant impact on citizens' behavior and attitude. Through this, it was confirmed that citizens continue to wear masks to protect themselves in an uncertain situation where the COVID-19 endemic has not been declared, despite the quarantine authorities' announcement of lifting the mandatory wearing. In a situation where crises such as COVID-19 are expected to repeat frequently in the future, it was concluded that it is important to build trust in the quarantine authorities.