• Journal of Digital Convergence
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• v.18 no.2
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• pp.277-284
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• 2020

Text mining is a representative method of big data analysis that extracts meaningful information from unstructured and large amounts of text data. Social media such as Twitter generates hundreds of thousands of data per second and acts as a one-person media that instantly and directly expresses public opinions and ideas. The traditional media are delivering informations, criticizing society, and forming public opinions. For this, we compare the responses of SNS with the responses of media on the issue of the termination of the Korea-Japan GSOMIA (General Security of Military Information Agreement), one of the domestic issues in the second half of 2019. Data collected from 201,728 tweets and 20,698 newspaper articles were analyzed by sentiment analysis, association keyword analysis, and cluster analysis. As a result, SNS tends to respond positively to this issue, and the media tends to react negatively. In association keyword analysis, SNS shows positive views on domestic issues such as "destruction, decision, we," while the media shows negative views on external issues such as "disappointment, regret, concern". SNS is faster and more powerful than media when studying or creating social trends and opinions, rather than the function of information delivery. This can complement the role of the media that reflects public perception.

• Journal of Korean Clinical Nursing Research
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• v.25 no.1
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• pp.80-90
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• 2019

Purpose: The aim of this study was to identify the research trends of articles published in the Journal of Korean Clinical Nursing Research from 2000 to 2017 by a text network analysis using keywords. Methods: This study analyzed 600 articles. The R program was used for text mining that extracted frequency, centrality rank, and keyword network. Results: From 2000 to 2009, keywords with high-frequency were 'nurse', 'pain', 'anxiety', 'knowledge', 'attitude', and so on. 'Pain', 'nurse', and 'knowledge' showed a high centrality. 'Fatigue' showed no high frequency but a high centrality. Keywords such as 'nurse', 'knowledge', and 'pain' also showed high frequency and centrality between 2010 and 2017. 'Hemodialysis' and 'intensive care unit' were added to keywords with high frequency and centrality during the period. Conclusion: The frequency and centrality of keywords such as 'nurse', 'pain', 'knowledge', 'hemodialysis', and 'intensive care unit' reflect the research trends in clinical nursing between 2000 and 2017. Further studies need to expand the keyword networks by connecting the main keywords.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.

• The Journal of Information Systems
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• v.8 no.2
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• pp.91-110
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• 1999

Recently, searching agent systems to help purchase of products between business and customer have been actively studied in Electronic Commerce(EC). However, the most of comparative searching agent systems are only provided customers with searching results by the keyword-based search, and is not support the efficient decision models to be selected products considering the customer's requirements. This paper proposes the decision agent system applied decision model as well as searching functions based on the keyword-input to be selected useful products in EC. The proposed decision agent system is consist of the user interface, provider interface, decision model. Especially, as the example of the decision model, this paper is designed and implemented the prototype of decision agent system which is normalized the searching data and value of customer's preference weight as to each attribute, and orderly provided customers with computed results. This agent system is also carried out sensitive analysis according to the reflection ratio of the each attribute.

• International Journal of Contents
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• v.17 no.4
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• pp.52-61
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• 2021

The 2020 US Presidential Election was a highly-anticipated moment for our global society. During the election period, the most intriguing issue was who would be the winner-Trump or Biden? Among the possible main themes of the 2020 election, from the COVID-19 pandemic to racism, this study focused on feminism ('women') as a main component of Biden's victory. To explore the character of Biden's supporters, this paper focused on internet spaces as a source of public opinion. To guide the data analysis, this study employed four indices from empirical studies on Big Data analytics: issue salience, attention diversity, emotional mentioning, and semantic cohesion. The main finding of this study was that the representative keyword 'women' appeared more prevalently within content related to Biden than Trump, and the keyword pairs indicated that female voters were the main reason for Trump's failure but the root cause of Biden's victory. The results of this study indicated the role of the internet as a forum for public opinion and a fountain of political knowledge, which requires more rigorous investigation by researchers.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• 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.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.105-111
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• 2022

Multiple-choice survey types have been mainly performed to analyze students' needs for online classes. However, in order to analyze the exact needs of students, unstructured data analysis by answer for essay question is required. Big data is applied in various fields because it is possible to analyze unstructured data. This study aims to investigate and analyze what students want subjects or topics for software lecture in general education that process on non-face-to-face online teaching methods. As for the experimental method, keyword analysis and association analysis of big data were performed with unstructured data by giving a subjective questionnaire to students. By the result, we are able to know the keyword what the students want for software lecture, so it will be an important data for planning and designing software lecture of liberal arts in the future as students can grasp the topics they want to learn.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.21-37
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• 2021

This study investigated the intellectual structure of the latest trends in Korean addiction research in the social sciences. A network analysis of keywords with co-word occurrence was performed on 172 papers from the KCI database based on the data from the year of 2019, and a total of 432 keywords were extracted. The network analysis was performed using several programs: Bibexcel, COOC, WNET, and NodeXL. As a result of the study, keywords related to addiction type, study subjects, research methods, and research variables were found, and a total of 20 clusters were identified. Furthermore, to identify and measure weighted networks, the relationships between each keyword were explored and discussed in detail through a network analysis of global centralities, local centralities, and betweenness centralities. The study indicated that the latest issues were focused on smartphone addiction and provided implications for the future research and practice that fields and topics of relationship addiction, food addiction, and work addiction should be more considered. Further, the study discussed the relationship between drug addiction-crime, alcohol addiction-family, and gambling addiction-motivation and the necessity of qualitative study.

• Tunnel and Underground Space
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• v.25 no.4
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• pp.303-319
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• 2015

Text mining is one of the branches of data mining and is used to find any meaningful information from the large amount of text. In this study, we analyzed titles and keywords of two SCI journals on rock engineering by using text mining to find major research area, trend and associations of research fields. Visualization of the results was also included for the intuitive understanding of the results. Two journals showed similar research fields but different patterns in the associations among research fields. IJRMMS showed simple network, that is one big group based on the keyword 'rock' with a few small groups. On the other hand, RMRE showed a complex network among various medium groups. Trend analysis by clustering and linear regression of keyword - year frequency matrix provided that most of the keywords increased in number as time goes by except a few descending keywords.