• Korea Trade Review
• /
• v.47 no.6
• /
• pp.189-209
• /
• 2022

This study aims to analyze Latin America and the Caribbean papers published in Korea during the past 2000-2020 years. Through this study, it is possible to understand the main subject and direction of research in Korea's Latin America and the Caribbean area. As the research mythologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. During 2011-2020 years, the most frequent keywords were changed from Neoliberalism and culture to policy education, and economy related words. The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, language, and Neoliberalism were the most important keywords during 2000-2010 years but economy, labor trade, industry, development became the most important keywords during 2011-2020 years in topics.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.8
• /
• pp.21-30
• /
• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.33-54
• /
• 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
• /
• v.12 no.4
• /
• pp.21-32
• /
• 2014

Recently, the number of Koreans who use smartphones has increased drastically; many use smartphones to learn English. In this study, one hundred Korean adult ELT (English language teaching) learners were surveyed to investigate their use of smartphones and factors influencing such use. For comparison, sixty-two students of a Korean cyber university were surveyed; these students were able to study using their smartphones in a smart campus environment. The research results showed that both groups positively used smartphones frequently, and that many intended to continue using them. With regard to ELT, both groups intended to learn English using their smartphones. Furthermore, they preferred certain types of ELT content: thirty-minute or less learning sessions, receptive English skills that focused on listening and reading, and short units of framed language items such as pronunciation and vocabulary. However, few of the respondents in both groups installed ELT apps on their smartphones, and few of the ELT apps satisfied them. The cyber university students responded similarly about smartphone use, although their responses regarding smartphone use for ELT purposes were less positive. These results indicate that the goal of cyber universities in achieving optimum learning outcomes through smart learning and the smart campus has not yet been realized.

• Korean journal of communication and information
• /
• v.45
• /
• pp.220-254
• /
• 2009

Presidential TV debates serve as an important instrument for the general viewers to evaluate the candidates’ character, to examine their policy, and finally to make an important political decisions to cast ballots. Every words candidates utter in the course of entire election campaign exert influence of a certain significance by delivering their ideas and by creating clashes with their respective opponents. This study focuses on the conceptual venue, coined as ‘stasis’ by ancient rhetoricians, in which the clashes take place, and examines the words selection made by each candidates, the manners in which they form stasis, call for evidence, educate the public, and finally create a legitimate form of political argumentation. The study applied computer based content analysis using KrKwic and UCINET software to analyze semantic networks among the candidates. The results showed three major candidates, namely Lee Myung Bak, Jung Dong Young, and Lee Hoi Chang, displayed separate patterns in their use of language, by selecting the words that are often neglected by their opponents. Apparently, the absence of stasis and the lack of speaking mutual language significantly undermined the effects of debates. Central questions regarding issues of North Korea failed to meet basic requirements, and the respondents failed to engage in effective argumentation process.

• Journal of Korean Society of Disaster and Security
• /
• v.12 no.4
• /
• pp.15-26
• /
• 2019

Anjunshinmungo (The safety e-report) has been in operation since 2014, and there are about 1 million cumulative reports by June 2019. This study analyzes the contents of more than 1 million safety newspapers reported at the present time of information age to determine how powerful and meaningful the people's voice and interest are. In particular, we are interested in forecasting ability. We wanted to check whether the report of the safety newspaper was related to possible disasters. To this end, the researchers received data reported in the safety newspaper as text and analyzed it by natural language analysis methodology. Based on this, the newspaper articles during the analysis of the safety newspaper were analyzed, and the correlation between the contents of the newspaper and the newspaper was analyzed. As a result, accidents occurred within a few months as the number of reports related to response and confirmation increased, and analyzing the contents of safety reports previously reported on social instability can be used to predict future disasters.

• Journal of Digital Convergence
• /
• v.14 no.6
• /
• pp.33-41
• /
• 2016

In this study, it was to prepare a plan for improving the employment rate of college students after identifying employment preparation behavior by grade level. Based on data collected in this study, frequency analysis was analyzed and in order to find out the difference of employment preparation behavior between grade level, Cross-analysis was performed. The results, all items showed that significant differences by grade level in preparation for employment actions such as choice of a double major, workers' experience in college education services, language training experience in foreign countries, setting career in college education services, setting a goal for employment, seeking for employment in college education services, participation in employment activity for researching job, education/training-related job, having an official license. Implications for activation direction of employment preparation of college student by grade level is as follows. First, it is necessary for encouraging actively employment intention of the students. Second, there is a need for institutional support for certification and expanding language training opportunities. Third, in the case of a local university, it is necessary for enhancing competency for employment through the promotion of basic education for students.

• Korean Educational Research Journal
• /
• v.37 no.1
• /
• pp.67-101
• /
• 2016

The purpose of this study is to explore and propose policy alternatives by examining students' educational experiences at Chungbuk National University (CBNU). Some of the outstanding research findings of the study as follows. Students at CBNU think that they are capable of team-working and utilizing internet while less so with foreign language skill. With regard to academic achievement, students responded that they have seen a relatively low outcome in foreign language competency. In terms of their job placement, CBNU students highly recognize their logical thinking skill, creativity, and activity, whereas lower satisfaction with social service, study abroad, and internship experience. For further development of the survey analysis, this study suggests additional items included to make a sophisticated analysis possible such as scholarship, part-time job, educational outcome. This is expected to allow researchers to tab into the effect of finance of CBNU students. More detailed information on students' characteristics also need to be added; collaborative learning, student faculty interaction, co-work with students from diverse background, etc., which would allow the analysis of the impact of extra-curricula activities.

• Cross-Cultural Studies
• /
• v.42
• /
• pp.131-163
• /
• 2016

The issue of migrants has always been a social problem in France. Particularly, migration issues from the Maghreb region to multicultural France are recognized as topics of confrontation and conflict. One of the many reasons would be that the Maghreb migrant issue is interpreted with a hostile or conflicting 'Arab-Islam' meaning in the West. Therefore, the naming of natives from this region as 'Arab-Maghrebis' has become a common phenomenon. However, deeper understanding of the Maghreb region will explain that such naming could produce a number of problems. This is because the native Maghrebis, not only in the Maghreb region but also in France, are Berbers. Among the various Berbers, the Kabyles are in many ways different from the Arabs in their language, culture, and way of thinking. Apart from the difference in their lifestyle, there is a deep anti-Arab sentiment within them that represents their resistance against the hundreds-of-years Arab domination. This feature has also been used as France's tool of colonial division policy during the colonization period. The purpose of this study is to discuss the settlement process of the Kabyles in France by paying attention to their features. The Kabyles were the first to migrate to France among the natives of the Maghreb region. This study focuses on the process of their migration and search for identity, as well as how they continued to form community and the Berber's cultural movement. Through these series of processes, it is possible to point out the fictitious claim in France and in Korea that Maghrebis are Arab-Muslims. Lastly, the current use of Berber language in France and Algeria is not a coincidental phenomenon, rather it has a connection with the prolonged cultural movement and the settlement process of the migrants in France.

• Journal of Digital Convergence
• /
• v.19 no.2
• /
• pp.447-456
• /
• 2021

This study analyzed 28 domestic and foreign studies related Prompts for Restructuring Oral Muscular Phonetic Targets treatment techniques from 1984 to 2020 to prepare basic data for the development of PROMPT intervention programs and examination tools. According to the analysis, continuous research has been conducted since 1984 when the prompt study was first started, and the method of research was 16 intervention studies, with the highest number of speech disorders, and the target age being 3 to 5 years old, the most frequently conducted for infancy. The treatment was the most frequent in the 16th sessions, and the activities were based on the Motor Speech Hierarchy(MSH), except for the subjects of the non-verbal autism spectrum disorder. According to the analysis of the dependent variables, 'speech production' was the most common, followed by 'speech motor control', 'articulation', and 'speech intelligibility' were highest. Combined with all these studies, it suggests that PROMPT, which are directly useful for exercise spoken word production, are effectively being used outside the country and that it is necessary to develop a PROMPT program that can be applied domestically, in Korea.