• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.59-65
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• 2021

This is a case study on the use of Albert Camus' La Peste, which has gained its popularity in today's generation of post-COVID as well as the use of big data analysis tools for major and elective classes. First, we asked students majoring in French to compare the use of vocabulary and the number of appearances for characters using big data analysis, for about 400 pages of the original text. As a result, we were able to confirm a similar relationship between Camus' Absurdism and the vocabulary used within La Peste, in addition to noting the heavy frequency of resistant characters. Students in elective classes were asked to read the literature in a Korean-translated version to determine the frequency of vocabulary and characters' appearances. Students were able to strongly relate to La Peste due to its commonality between COVID and the plague in the literature. We also received high levels of class satisfaction regarding the use of big data analysis tools. The students showed a positive response both towards choosing La Peste as the work of literature and using big data, the main tool in the Fourth Industrial Evolution. We were able to identify good results even in a non-contact environment, as long as the literature does not rely on traditional methods but rather lectures to reflect current situations.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.362-373
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• 2021

This study sought to explore the enhancement of the introduction of teaching and learning methods for Problem Based Learning (PBL) and the evaluation factors to evaluate them effectively through an understanding lecture in Cultural Content Planning. It was intended to incorporate a practical zero-volume education methodology of problem-oriented learning and sufficient leading learning to reflect storytelling in the entire process of completing a cultural content with culture, cultural content, and content planning. To this end, the role of teaching methods should be faithful to ensure that teamwork and cooperation can be done organically according to the educational field, practice and situation. Students who take classes were asked to meet demand, reflect it through surveys, apply real-world problems, and acquire the entire course. Learners had to cooperate with each other until planning cultural content and completing the results through classes, and they evaluated themselves and colleagues in teamwork until the last result was completed from creative ideas. The results were shared together and the students were able to investigate the necessary PBL evaluation factors for themselves, and the prior research and survey on the method of PBL evaluation was conducted to derive the factors of understanding of cultural content planning. The derived assessment elements were able to identify priorities between the assessment elements using basic statistics, word cloud analysis, and AHP analysis. The components of the assessment derived were communication skills, basic knowledge, reasoning process, expertise, and evaluation techniques. Through this article, I was able to lead the understanding of cultural content planning to problem-oriented learning classes and encourage students to be familiar and smooth.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.343-352
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• 2021

The purpose of this article is to study the negative factors reflected in the will and the factors of well aging as an alternative. The survey data was 36 wills published in the media such as newspapers and broadcasting between 2008 and 2020. As a result, various aspects of negative factors were found in youth wills between the ages of 13 and 34. In middle-aged wills between the ages of 35 and 49, female was not found. The negative factors in the socio-economic aspects were remarkable in the wills of adulthood between the ages of 50 and 64. All the writers of wills over the age of 65 were women, and their writings were strongly linked to the spiritual side. In view of these results, the will explored in this study can paradoxically become a proposal for a complete life. The will is a record with the potential of well aging. Sources of the suicide note included daily newspaper, broadcasting and local media. This study analyse the age and gender and the negative factors reflected in the will, by using the physical aspect, the mental aspect, and the socio-economic aspect as the methodology. In addition, the frequency of words and expressions exposed in the will were analyzed and keywords were created in word cloud.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.99-114
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• 2019

In this study we examined the preferences of eight college living factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the analysis results of text mining were visualized as word cloud. The college life factors included eight topics that were closely related to students: 'my present', 'my 10 years later', 'friendship', 'college festival', 'student restaurant', 'college dormitory', 'KNCAF', and 'long-term field practice'. In the text submitted by the students, we have established a dictionary of positive words and negative words to evaluate the preference by classifying the emotions of positive and negative. As a result, KNCAF students showed more than 85% positive emotions about the theme of 'student restaurant' and 'friendship'. But students' positive feelings about 'long-term field practice' and 'college dormitory' showed the lowest satisfaction rate of not exceeding 60%. The rest of the topics showed satisfaction of 69.3~74.2%. The gender differences showed that the positive emotions of male students were high in the topics of 'my present', 'my 10 years later', 'friendship', 'college dormitory' and 'long-term field practice'. And those of female were high in 'college festival', 'student restaurant' and 'KNCAF'. In addition, using text mining technique, the main words of positive and negative words were extracted, and word cloud was created to visualize the results.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.159-178
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• 2021

This study analyzed issues and trends related to ports with 86,611 news articles for the 30 years from 1991 to 2020, using BIGKinds, a big data news analysis service. The analysis was based on keyword analysis, word cloud, relationship diagram analysis offered by BIG Kinds. Analysis results of issues and trends on ports for the last 30 years are summarized as follows. First, during Phase 1 (1991-2000), individual ports such as Busan, Incheon, and Gwangyang ports tried to strengthen their own competitiveness. During Phase 2 (2001-2010), efforts were made on gaining more professional and specialized port management abilities by establishing the Busan Port Authority in 2004, the Incheon Port Authority in 2005, and the Ulsan Port Authority in 2007. During Phase 3 (2011-2020), the promotion of future-oriented, eco-friendly, and smart ports was major issues. Efforts to reduce particulate matters and pollutants produced from ports were accelerated, and an attempt to build a smart port driven by port automation and digitalization was also intensified. Lastly, in 2020, when the maritime sector was severely hit by the unexpected shock of the COVID-19 pandemic, a microscopic analysis of trends and issues in 2019 and 2020 was made to look into the impact the pandemic on the maritime industry. It was found that shipping and port industries experienced more drastic changes than ever while trying to prepare for a post-pandemic era as well as promoting future-oriented ports. This study made policy suggestions by analyzing port-related news articles and trends, and it is expected that based on the findings of this research, further studies on enhancing the competitiveness of ports and devising a sustainable development strategy will follow through a comparative analysis of port issues of different countries, thereby making further progress toward academic research on ports.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.