• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.32-42
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• 2024

In this study, 1,474 effective patents were derived for quantitative analysis of five major countries, including Korea, China, Japan, the United States and Europe, for marine space policy simulator technology used as a support for integrated marine space management means, and domestic technology competitiveness and domestic and foreign technology trends were identified through annual and national patent application trends and word cloud analysis. This diagnosed the need for active policy support for research and development of marine space policy simulator technology at the government level and preparation through linkage strategies such as patent application consideration and standardization preoccupation for surrounding technologies to prepare for China-led market monopoly and preoccupation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.87-97
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• 2018

The traffic accident reports that are generated by the Traffic Broadcasting Networks(TBN) are unstructured data. It, however, has the value as some sort of real-time traffic information generated by the viewpoint of the drives and/or pedestrians that were on the roads, the time and spots, not the offender or the victim who caused the traffic accidents. However, the traffic accident reports, which are big data, were not applied to traffic accident analysis and traffic related research commonly. This study adopting text-mining technique was able to provide a clue for utilizing it for the impacts of traffic accidents. Seven years of traffic reports were grasped by this analysis. By analyzing the reports, it was possible to identify the road names, accident spot names, time, and to identify factors that have the greatest influence on other drivers due to traffic accidents. Authors plan to combine unstructured accident data with traffic reports for further study.

• Journal of Korean Society of Archives and Records Management
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• v.16 no.1
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• pp.89-120
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• 2016

This study aims to understand the concept and changes of the records management of InterPARES based on the analysis of background, main research interests, and major achievements of IP3 and ITrust. To this end, this study conducted a content analysis of IP3 and ITrust to drive main keywords. This study also utilized word clouds from IP project titles. In addition, a comparative analysis of IP3 and ITrust was conducted based on the environment, scope, core research areas, keywords, objectives, and record management life cycle perspectives. The research identified that InterPARES research was widely expanding the content and subject areas of the study: 1) to apply across the life cycle, as well as long-term preservation; 2) to focus on the concept of trust as well as the concept of authenticity; and 3) to include the concept of the Internet, digital forensics, and the open government along with electronic records.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.111-122
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• 2021

This study analyzed user reviews of Dongbaekjeon and Incheoneum app, which are representative local currencies in Korea, to identify the positive/negative factors of local currency users, and established a marketing strategy based on this. App user reviews were classified into positive and negative based on the star rating, and word cloud, topic modeling, and social network analysis were performed, respectively. As a result, in the negative reviews of Dongbaekjeon and Incheoneum, dissatisfaction with app use and card issuance appeared in common. In positive reviews, keywords such as 'local economy' and 'small business owners' along with satisfaction with 'cashback' appeared. It means that local currency users perceived that their consumption support local economy, and they felt satisfaction in using local currency. Based on the satisfaction/dissatisfaction factors identified as a result of the analysis of this study, we identified what needs to be improved and to be strengthened, and appropriate marketing strategies were established. The text mining method used in this study and research results can provide meaningful information about local currencies to public officials and marketers in charge of local currencies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.101-107
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• 2020

This paper aims to compare and analyze the major keywords according to the political orientation of progressive and conservative newspapers by utilizing the big data of the four major domestic daily newspapers related to COVID-19, which has entered a long-term war. To this end, 93,917 news reports from Jan. 20 to Sept. 15, 2020 were divided into four stages and the major keywords of the four newspapers were implemented and analyzed in WordCloud. According to the analysis, the conservative newspaper focused on the government's response, criticism, and China's responsibility by mentioning the keywords "government," "president," "state of affairs" and "mask" more than the progressive newspaper, while the progressive newspaper uses keywords that emphasize the seriousness of the disease and the occurrence of a dangerous situation. The Chosun Ilbo found that the use of various keywords during the massive outbreak of collective infections (2.18-5.15), and that the JoongAng Ilbo used keywords criticizing government policies in relation to reports of infectious diseases such as COVID-19, but also used keywords that emphasize the seriousness of diseases used by progressive newspapers and the occurrence of dangerous situations.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.111-119
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• 2022

The purpose of this paper is to examine the change in perception of 3D virtual fashion before and after COVID-19 using big data analysis. The data collection period is from January 1, 2017, before the outbreak of COVID-19, to October 30, 2022, after the outbreak. Big data was collected for key words related to 3D virtual fashion extracted from social media such as Naver, Daum, Google, and YouTube using Textom. After the collected words were refined, word cloud, word frequency, connection centrality, network visualization, and CONCOR analysis were performed. As a result of extracting and analyzing 32,461 words with 3D virtual fashion as a keyword, the frequency and centrality of fashion, virtual, and technology appeared the highest, and the frequency of appearance of digital, design, clothing, utilization, and manufacturing was also high. Through this, it was found that 3D virtual fashion is being used throughout the industry along with the development of technology. In particular, the key words that stand out the most after COVID-19 are metaverse and 3D education, which are in high demand in the fashion industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1571-1576
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• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.181-188
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• 2024

Fueled by the digital transformation era, the 150th anniversary of Marcel Proust's birth (2021) and 100th anniversary of his death (2022) witnessed a surge in digital humanities research. This goes beyond supplementing traditional methods; it fosters new approaches like Nicolas Lagonneau's 'Proustonomics' website (archiving online/offline Proust discourse) and 'Proustographe' (quantifying and visualizing data related to Proust). The Buffalo Proust Project (2021) provided online access to materials on his life and works, while the Corr-Proust project digitized his correspondence. While Korea lacks established digital Proust research, recent analysis of academic paper vocabulary (through word frequencies and word clouds) reveals significant thematic and quantitative development around 2000, paving the way for future Korean ventures in this exciting field. Digital humanities research offers the potential to unearth new research topics, enhance efficiency, and promote international collaboration, ultimately leading to a deeper understanding of Proust and groundbreaking advancements in the field.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.