• The Journal of Korean Association of Computer Education
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• 제22권3호
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• pp.67-78
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• 2019

The purpose of this study is to explore information ethics issues based on academic big data from Web of Science (WoS) and to provide implications for information ethics education in informatics subject. To this end, 318 published papers from WoS related to information ethics were text mined. Specifically, this paper analyzed the frequency of key-words(TF, DF, TF-IDF), information ethics issues using topic modeling, and frequency of appearances by year for each issue. This paper used 'tm', 'topicmodel' package of R for text mining. The main results are as follows. First, this paper confirmed that the words 'digital', 'student', 'software', and 'privacy' were the main key-words through TF-IDF. Second, the topic modeling analysis showed 8 issues such as 'Professional value', 'Cyber-bullying', 'AI and Social Impact' et al., and the proportion of 'Professional value' and 'Cyber-bullying' was relatively high. This study discussed the implications for information ethics education in Korea based on the results of this analysis.

• Journal of Convergence for Information Technology
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• 제12권3호
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• pp.25-37
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• 2022

The purpose of this study is to comprehensively check trends in policies, discourses, educational directions and contents, and social issues by deriving the subjective characteristics of peace education and unification education based on big data analysis. The results of this study are as follows. First, 'peace', 'unification', 'education', 'research', 'student', 'school', 'teacher', 'target', and 'Korean Peninsula' were commonly important keywords in peace education and unification education. Second, the top topic of peace education was 'peace education and civic education', and the top topic of unification education was ' sympathy and participation in unification education'. Third, topics that show an upward trend by regime in peace education were 'World Peace and Human Rights' and 'Object and Direction of Peace Education', and 'Subject of Unification Education' as topics that showed an upward trend by regime in unification education. Fourth, in peace education, the centrality of 'peace', 'education', 'student', 'school', and 'peace education' was high, and in unification education, 'unification', 'education', 'unification', 'school', and 'teacher' were high. Based on these results, it was intended to expand the horizon of understanding peace education and unification education, and to provide meaningful implications for establishing policies and conducting follow-up studies.

• Journal of the Korean Institute of Gas
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• 제24권2호
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• pp.50-55
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• 2020

The heuristics accumulated in the field activities of firefighters were reviewed through big data analysis of fire occurrences in Gyeonggi-do and researched to be utilized for proper fire prevention activities according to time, day, and target through quantitative modeling. Empirical rules with high sympathy were collected through direct interviews with firefighters. Among them, the rule of thumb that "Friday is the most fire-prone" is considered to be the most important in terms of fire monitoring and prediction. A big data comparison analysis was conducted, including the number of fires and damages that occurred in Gyeonggi-do in 2018. Furthermore, fire occurrence patterns by region, day of the week, time of day, and building type were derived. Regarding empirical rules that have been validated through research, relatively inexperienced firefighters also can make decisions by relying on refined quantitative predictive modeling and empirical rules including local government and time-based factors that reflect big fire occurrence data.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제25권5호
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• pp.731-740
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• 2021

This study applied LDA topic modeling analysis to collect and analyze news media big data related to face masks in the three waves of the COVID-19 pandemic in Korea. The results empirically show that media reports focused on mask production and distribution policies in the first wave and the mandatory mask wearing in the second wave. In contrast, more reports on trivial, gossipy events consist of the media coverage in the second and third waves. The findings imply that Korea's governmental interventions to address the shortage of face masks and to regulate mask wearing were successful relatively in a short time. In contrast, the study also reports that there may be relative less number of science-based news reports like the ones on the effectiveness of face masks or different levels of filter types. This study exemplifies how a big data analysis can be applied to evaluate and enhance public health communication.

• Knowledge Management Research
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• 제17권2호
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• pp.165-185
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• 2016

Though Korea has received attention through an exceptional economic growth and the big K-POP fever all over the world, its happiness level is not so high. Therefore, this research aims to find not only the Korean' s condition of the happiness and unhappiness, but also the way to enhance their happiness. We collected various web data(89,127 cases from 2013/01 to 2014/12) through searching our own 26 keywords based on Alderfer's ERG Theory. Also, we tried to analyze the subjects related to happiness and unhappiness by using LDA topic modeling. As the result, the condition of happiness and unhappiness were the top topics extracted from each field. We conducted the second detailed analysis based on the data of condition of the happiness and unhappiness which are the top topics of the previous analysis. From the second analysis result, we proposed several ways to enhance happiness from the perspective of government, corporate, family, education, social welfare.This paper is meaningful because it catches the condition of happiness and unhappiness based on a real web data as well as transform the data into the knowledge. Also, this paper provides the practical methods from the view from all walks of life that may enhance happiness and relieve unhappiness.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.781-790
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• 2020

This study's primary purpose is to explore the psychological capital roles and personality traits of Big-Five in predicting OCB (organizational citizenship behavior) and performance of task in Indonesia's electricity sector. The data were gathered from the employees of four major cities in Indonesia, in Southeast Sulawesi, comprising 246 employees. The data were analyzed utilizing a PLS (partial least squares) based SEM (structural equation modeling) technique. The findings indicate that the psychological capital and personality traits of Big-Five relate significantly to OCB and the performance of task. Nevertheless, against our expectations, OCB does not significantly relate to the performance of task. This study also discusses the findings' further implications. In terms of practical implications, the findings of this research stipulate that psychological capital and Big-Five personality traits aimed to improve employee performance and can be most effective if specifically targeted at OCB. Given that both variables play an important role to promote OCB, caring training initiatives that focus on mutual help can be very valuable for organizational improvement. In a managerial perspective, organizations can increase OCB by conducting open communication strategies between managers and employees to further stimulate and strengthen the ability of employees to display extra-role behaviors.

• Journal of Digital Convergence
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• 제19권8호
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• pp.33-42
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• 2021

As smartphone-based tourism platforms have become active, policy establishment and service enhancement using review data are being made in various fields. In the case of the preceding studies using tourism review data, most of the studies centered on domestic tourists were conducted, and in the case of foreign tourist studies, studies were conducted only on data collected in some languages and text mining techniques. In this study, 3,515 review data written by foreigners were collected by designating the "Daegu attractions" keyword through the online review site. And LDA-based topic modeling was performed to derive tourism topics. The spatial approach through global and local spatial autocorrelation analysis for each topic can be said to be different from previous studies. As a result of the analysis, it was confirmed that there is a global spatial autocorrelation, and that tourist destinations mainly visited by foreigners are concentrated locally. In addition, hot spots have been drawn around Jung-gu in most of the topics. Based on the analysis results, it is expected to be used as a basic research for spatial analysis based on local government foreign tourism policy establishment and topic modeling. And The limitations of this study were also presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제46권4호
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• pp.15-31
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• 2023

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제21권2호
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• pp.195-203
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• 2021

Recently, big data, a major technology in the IT field, has been expanding into various industrial sectors and research on how to utilize it is actively underway. In most Internet industries, user reviews help users make decisions about purchasing products. However, the process of screening positive, negative and helpful reviews from vast product reviews requires a lot of time in determining product purchases. Therefore, this paper designs and implements a system that analyzes and aggregates keywords using LDA, a big data analysis technology, to provide meaningful information to users. For the extraction of document topics, in this study, the domestic book industry is crawling data into domains, and big data analysis is conducted. This helps buyers by providing comprehensive information on products based on user review topics and appraisal words, and furthermore, the product's outlook can be identified through the review status analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제23권7호
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• pp.764-770
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• 2019

In this research, text mining techniques were used to analyze the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers and use topic modeling techniques based on LDA algorithm implemented in R language for data analysis. As a result of perplexity analysis on the collected papers, nine topics were determined optimally and nine representative topics of the collected papers were extracted using the Gibbs sampling method. As a result, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been conducted on the changes due to the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.