• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.83-107
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• 2020

Selecting business charts based on the nature of the data and the purpose of the visualization is useful in business analysis. However, current visualization tools lack the ability to help choose the right business chart for the context. Also, soliciting expert help about visualization methods for every analysis is inefficient. Therefore, the purpose of this study is to propose an accessible method to improve business chart productivity by creating rules for selecting business charts from online published documents. To this end, Korean, English, and Chinese unstructured data describing business charts were collected from the Internet, and the relationships between the contexts and the business charts were calculated using TF-IDF. We also used a Galois lattice to create rules for business chart selection. In order to evaluate the adequacy of the rules generated by the proposed method, experiments were conducted on experimental and control groups. The results confirmed that meaningful rules were extracted by the proposed method. To the best of our knowledge, this is the first study to recommend customizing business charts through open unstructured data analysis and to propose a method that enables efficient selection of business charts for office workers without expert assistance. This method should be useful for staff training by recommending business charts based on the document that he/she is working on.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.92-104
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• 2021

This study aims to analyze online news articles to identify social issues related to patient safety and compare the changes in these issues before and after the implementation of the Patient Safety Act. This study performed text mining through the R program, wherein 7,600 online news articles were collected from January 1, 2010, to March 5, 2020, and examined using keyword analysis, topic modeling, and word co-occurrence network analysis. A total of 2,609 keywords were categorized into 8 topics: "medical practice", "medical personnel", "infection and facilities", "comprehensive nursing service", "medicine and medical supplies", "system development and establishment for improvement", "Patient Safety Act" and "healthcare accreditation". The study revealed that keywords such as "patient safety awareness", "infection control" and "healthcare accreditation" appeared before the implementation of the Patient Safety Act. Meanwhile, keywords such as "patient safety culture". and "administration and injection" appeared after the act's implementation with improved ranking of importance pertaining to nursing-related terminology. Interest in patient safety has increased in the medical community as well as among the public. In particular, nursing plays an important role in improving patient safety. Therefore, the recognition of patient safety as a core competency of nursing and the persistent education of the public are vital and inevitable.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.51-69
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• 2021

Purpose - Based on literature studies of movie reviews and movie ratings, this study raised two research questions on the contents of online word of mouth and the number of movie screens as mediator variables. Research question 1 wanted to figure out which topics of word groups had a positive or negative impact on movie ratings. Research question 2 tried to identify the role of the number of movie screens between movie ratings and box office outcomes. Design/methodology/approach - Through R program, this study collected about 82,000 movie reviews and movie ratings posted on Naver's movie website to examine the role of online word of mouths and movie screen counts in 10 movies that were considered commercially unsuccessful with fewer than 2 million viewers despite securing about 1,000 movie screens. To confirm research question 1, topic modeling, a text mining technique, was conducted on movie reviews. In addition, this study linked the movie ratings posted on Naver with information of KOBIS by date, to identify the research question 2. Findings - Through topic modeling, 5 topics were identified. Topics found in this study were largely organized into two groups, the content of the movie (topic 1, 2, 3) and the evaluation of the movie (topics 4, 5). When analyzing the relationship between movie reviews and movie ratings with 5 mediators identified in topic modeling to probe research question 1, the topic word groups related to topic 2, 3 and 5 appeared having a negative effect on the netizen's movie ratings. In addition, by connecting two secondary data by date, analysis for research question 2 was implemented. The outcomes showed that the causal relationship between movie ratings and audience numbers was mediated by the number of movie screens. Research implications or Originality - The results suggested that the information presented in text format was harder to quantify than the information provided in scores, but if content information could be digitalized through text mining techniques, it could become variable and be analyzed to identify causality with other variables. The outcomes in research question 2 showed that movie ratings had a direct impact on the number of viewers, but also had indirect effects through changes in the number of movie screens. An interesting point is that the direct effect of movie ratings on the number of viewers is found in most American films released in Korea.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.460-469
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• 2020

This study analyzes online news articles and cafe articles on the '5-day Rotation Mask Distribution System', which is emerging as a recent issue due to the COVID-19 incident, to identify the mass media and social media agendas containing media and public reactions. This study figured out the difference between mass media and social media. For analysis, we collected 2,096 full text articles from Naver and 1,840 posts from Naver Cafe, and conducted word frequency analysis, word cloud, and LDA topic modeling analysis through data preprocessing and refinement. As a result of analysis, social media showed real-life topics such as 'family members' purchase', 'the postponement of school opening', ' mask usage', and 'mask purchase', reflecting the characteristics of personal media. Social media was found to play a role of exchanging personal opinions, emotions, and information rather than delivering information. With the application of the research method applied to this study, social issues can be publicized through various media analysis and used as a reference in the process of establishing a policy agenda that evolves into a government agenda.

• The Journal of Information Systems
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• v.26 no.1
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• pp.21-51
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• 2017

Recently there have been an increasing number of companies suffering a negative brand image in the major media. Thompson et al. (2006) defined this as "$Doppelg{\ddot{a}}nger$ Brand Image." The images mentioned above have been created and propagated on Internet communities, which are one of the major paths of online spreading. This study will empirically analyze the effect of each $Doppelg{\ddot{a}}nger$ brand image on the customer's brand attitude, using a text-mining method focusing on "A company"'s case. This study will also cover the change in customer brand attitudes related to the company's correspondence in a situation in which the $Doppelg{\ddot{a}}nger$ brand image exists. In addition, the study will determine the presence of a priming effect after the spread of the $Doppelg{\ddot{a}}nger$ brand image. To that end, we collected 974 comments from 94,889 posts and A's official blogs related to A from B community, the largest automobile community site in Korea. Through this investigation, we obtained the following results. First, there was a significant difference in the ratio of negative sentiment of internet community before and after $Doppelg{\ddot{a}}nger$ brand image. Second, with regard to the topic modeling, the ratio of articles including negative topics increased and the other article ratio decreased over time. Finally, we found that there is a priming effect about negative brand image of "A company."

• Information Systems Review
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• v.8 no.1
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• pp.181-201
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• 2006

As the Internet spreads, many people have interests in e-CRM and product recommender systems, one of e-CRM applications. Among various approaches for recommendation, collaborative filtering and content-based approaches have been investigated and applied widely. Despite their popularity, traditional recommendation approaches have some limitations. They require at least one purchase transaction per user. In addition, they don't utilize much information such as demographic and specific personal profile information. This study suggests new hybrid recommendation model using two data mining techniques, association rule and classification, as well as intelligent agent to overcome these limitations. To validate the usefulness of the model, it was applied to the real case and the prototype web site was developed. We assessed the usefulness of the suggested recommendation model through online survey. The result of the survey showed that the information of the recommendation was generally useful to the survey participants.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.61-69
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• 2015

We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

• Journal of Korean Library and Information Science Society
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• v.51 no.3
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• pp.251-279
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• 2020

This study noted the recent impact of Coronavirus Disease-19 (Corona 19) on the environment surrounding the library, and investigated the libraries' response activities. In addition, related issues on news media and social media were detected based on text mining techniques to engage environmental changes surrounding the library. Key issues were derived from 1,852 news reports on the library related to the Corona 19 situation and 227,983 tweets related to the library during the Corona 19 epidemic. Through this, implications were derived: prolonged 'Untact' situations, increased e-book lending, improved expectations for online services and librarians, and re-conceptualized library space. In addition, the direction of future services was discussed by selecting representative examples of library services provided in the non-face-to-face (untact) situation and dividing them into books, services, and spaces.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.239-252
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• 2020

By using text mining, we explore the factors responsible for an increase in SUV preference. We collected 32,679 posts related to SUVs from "Bobaedream," the largest online automobile community in South Korea, and applied the LDA topic model. While previous studies have explained the SUV boom as an individual's risk aversion strategy from crime, the result shows that the topic of 'Safety' appears to be an important factor in the SUV discourse in the context of a car accident and high-speed driving situation. To conclude, the consumption of SUVs in Korean society serves as a mean to prevent anxiety and danger to individuals when driving. We insist that decreasing social trust, caused by an increase in inequality, underlies the perception of risk on the road.