• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.9-15
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• 2019

Artiodactyla, which is an even-toed mammal, widely inhabits worldwide. In recent years, wild Artiodactyla species have attracted public attention due to the rapid increase of crop damage and road-kill caused by wild Artiodactyla such as water deer and wild boar and the decrease of some species such as long-tailed goral and musk deer. In spite of such public attention, however, there have been few studies on Artiodactyla in Korea, and no studies have focused on the trend analysis of Artiodactyla, making it difficult to understand actual problems. Many recent studies on trend used text-mining and co-occurrence analysis to increase objectivity in the classification of research subjects by extracting keywords appearing in literature and quantifying relevance between words. In this study, we analyzed texts from research articles of three countries (Korea, China, and Japan) through text-mining and co-occurrence analysis and compared the research subjects in each country. We extracted 199 words from 665 articles related to Artiodactyla of three countries through text-mining. Three word-clusters were formed as a result of co-occurrence analysis on extracted words. We determined that cluster1 was related to "habitat condition and ecology", cluster2 was related to "disease" and cluster3 was related to "conservation genetics and molecular ecology". The results of comparing the rates of occurrence of each word clusters in each country showed that they were relatively even in China and Japan whereas Korea had a prevailing rate (69%) of cluster2 related to "disease". In the regression analysis on the number of words per year in each cluster, the number of words in both China and Japan increased evenly by year in each cluster while the rate of increase of cluster2 was five times more than the other clusters in Korea. The results indicate that Korean researches on Artiodactyla tended to focus on diseases more than those in China and Japan, and few researchers considered other subjects including habitat characteristics, behavior and molecular ecology. In order to control the damage caused by Artiodactyla and to establish a reasonable policy for the protection of endangered species, it is necessary to accumulate basic ecological data by conducting researches on wild Artiodactyla more.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Korean Journal of Heritage: History & Science
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• v.56 no.2
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• pp.264-279
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• 2023

The 50th anniversary of the UNESCO World Heritage Convention was in 2022. In order to reflect on the present and future of the meaning of World Heritage, this paper examines the development and changes of the UNESCO World Heritage system. After promulgating the convention in 1972, the UNESCO World Heritage system prioritized the protection of heritage sites in the world that were at risk due to armed conflicts and natural disasters to bequeath heritage to the next generation. In addition, the UNESCO World Heritage's emphasis on Outstanding Universal Value represents the particular culture of human beings formed during a certain period of time, and acts as a significant source of soft power in public diplomacy. The UNESCO World Heritage might be perceived as a shared heritage that has not only become a channel to understand various national values, but also an effective medium to convey one of UNESCO's main principles, that is, peacebuilding. However, the UNESCO World Heritage is now at the center of conflicts of heritage interpretation between many stakeholders related to invisible wars, such as cultural wars, memory wars, and history wars as the social, political, and cultural contexts concerning World Heritage have dramatically shifted with the passing of time. Paying attention to such changing contexts, this paper seeks to understand the main developments in UNESCO World Heritage's discourse concerning changes to the World Heritage Operation Guidelines and heritage experts' meetings by dividing its 50-year history into five phases. Next, this paper analyzes the main shifts in keywords related to UNESCO World Heritage through UNESDOC, which is a platform on which all UNESCO publications are available. Finally, this paper discusses three main changes of UNESCO World Heritage: 1) changes in focus in World Heritage inscriptions, 2) changes in perception of World Heritage protection, and 3) changes of view on the role of the stakeholders in World Heritage. It suggests new emerging issues regarding heritage interpretation and ethics, climate change, and human rights.

• Journal of Chest Surgery
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• v.40 no.3 s.272
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• pp.159-167
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• 2007

Background: Vocabularies originating from Chinese characters constitute an important common factor in the medical terminologies used 3 eastern Asian countries; Korea, China and Japan. This study was performed to comparatively analyze the medical terminologies of these 3 countries in the field of cardiopulmonary bypass (CPB) and; thereby, facilitate further understanding among the 3 medical societies. Material and Method: A total of 129 English terms (core 85 and related 44) in the field of CPB were selected and translated into each country's official terminology, with help from Seoul National University Hospital (Korea), Tokyo Michi Memorial Hospital(Japan), and Yanbian Welfare Hospital and Harbin Children Hospital (China). Dictionaries and CPB textbooks were also cited. In addition to the official terminology used in each country, the frequency of use of English terms in a clinical setting was also analyzed. Result and Conclusion: Among the 129 terms, 28 (21.7%) were identical between the 3 countries, as based on the Chinese characters. 86 terms were identical between only two countries, mostly between Korea and Japan. As a result, the identity rate in CPB terminology between Korea and Japan was 86.8%; whereas, between Korea and China and between Japan and China the rates were both 24.8%. The frequency of use of English terms in clinical practices was much higher in Korea and Japan than in China. Despite some inherent limitations involved in the analysis, this study can be a meaningful foundation in facilitating mutual understanding between the medical societies of these 3 eastern Asian countries.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Korean Journal of Ecology and Environment
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• v.51 no.4
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• pp.268-286
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• 2018

The eutrophication and algal blooms by harmful cyanobacteria (CyanoHAs) and freshwater redtide (FRT) that severely experiencing in typical regulated weir system of the Nakdong River are one of the most rapidly expanding water quality problems in Korea and worldwide. To compare with the factors of rainfall, hydrology, and dominant algae, this study explored spatiotemporal variability of the major water environmental factors by weekly intervals in eight weir pools of the Nakdong River from January 2013 to July 2017. There was a distinct difference in rainfall distribution between upstream and downstream regions. Outflow discharge using small-scale hydropower generation, overflow and fish-ways accounted for 37.4%, 60.1% and 2.5%, respectively. Excluding the flood season, the outflow was mainly due to the hydropower release through year-round. These have been associated with the drawdown of water level, water exchange rate, and the significant impact on change of dominant algae. The mean concentration (maximum value) of chlorophyll-a was $17.6mg\;m^{-3}$ ($98.2mg\;m^{-3}$) in the SAJ~GAJ and $29.6mg\;m^{-3}$ ($193.6mg\;m^{-3}$) in the DAS~HAA weir pools reaches, respectively. It has increased significantly in the downstream part where the influence of treated wastewater effluents (TWEs) is high. Indeed, very high values (>50 or $>100mg\;m^{-3}$) of chlorophyll-a concentration were observed at low flow rates and water levels. Algal assemblages that caused the blooms of CyanoHAs and FRT were the cyanobacteria Microcystis and the diatom Stephanodiscus populations, respectively. In conclusion, appropriate hydrological management practices in terms of each weir pool may need to be developed.

• Journal of Korean Academy of Nursing
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• v.4 no.3
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• pp.33-56
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• 1974

간호를 장래의 전문직으로 택하려고 공부하는 간호학생들은 완전한 성인으로서의 발달 과정중 후기 성년기에 속한다. 이시기는 자아를 발견하고 인간이 무엇을 믿으며 인간의 가치가 무엇인가를 추구하는 중요한 시기이다. 다시 말해서 어른과 어린이의 과도기에 서서 자신의 이상적 가치와 기성사회의 기존 가치를 잘 융화시켜 독립된 인간으로서 성숙하려는 노력의 시기이다. 그러므로 성년기의 갈등은 인생의 어느 시기보다도 그 정도가 심하게 나타난다. 간호학생들은 이상의 일반적인 성년기 발달의 요구 외에도 간호대학이라는 특수한 배움의 여건 때문에 좀 더 심각한 문제에 대두된다. 특히 소아과 간호대학이라는 실습환경은 여러 가지 복잡한 병실 사정으로 많은 긴장감을 주는 학습경험이다. 어린이의 간호에는 그들의 발달과정에 따른 다양한 역활이 요구된다. 또한 병원이라는 낯선 환경과 어머니를 떨어져야하는 두려움으로 불안한 어린이와 그 어린이의 불안과 두려움으로 인해 우울과 죄의식에 있는 어머니의 간호는 여러 면에서 성년기 학생들에게 긴장감을 일으키게 하는 요인이 된다. 본 연구의 문헌조사는 주로 미국 문헌에 나타난 간호 대학생들의 성년기 성숙을 위한 발달의 요구와, 소아병실의 복잡한 여건으로 발생되는 긴장감을 다루고 있다. 문헌을 기초로 하여 저자는 긴장감을 주는 간호활동을 크게 다섯 부군으로 묶었다. 1. 어린이 환자의 신체적 간호, 2. 어린이 환자나 부모와의 원만한 대화와 상호관계를 위한 간호활동 3. 소아병실에서 요구되는 다양한 간호원의 역활 4. 어린이나 부모의 간호에 대한 의뢰심 5. 간호의 가치나 이상적 간호. 연구방법으로는 49개의 폐쇄식 질문 항목을 가진 질문지를 사용하였다. 질문 항목들은 문헌연구에서 소아과 간호학 실습시 학생들이 긴장감을 느낀다고 밝혀진 내용들이 다. 학생들은 자신의 경험을 "긴장감이 없었다. ""긴장감이 있었다. ""심한 긴장감이 있었다. ""실습 중 경험이 없었다. "의 사항 중 택일을 하게 되어 있다. 연구대상으로는 모 대학교 간호대학 학생으로 산부인과 간호학 실습을 마친 후 소아과 간호학 실습 8주를 완료한 4학년 학생 42명이었다. 자료분석의 결과는 대부분의 학생들에게 소아과 간호학 실습은 긴장감을 주는 경험이 있다고 나타났다. 다음은 연구결과 주목할만한 몇 가지 사항들이다. 1. 어린이 환자의 신체적 간호는 성년기 학생들에게 긴장감을 주는 실습 경험이었다. 특별히 심하게 긴장감을 주었던 간호활동은 어린이환자의 상태가 중한 경우로, 장기간 앓는 아이, 선천성 기형이 있는 아이, 회복이 불가능하여 죽게될 아이나 사망하는 경우의 어린이 간호였다. 이 결과는 간호학의 기본과정 즉, 기초간호학이나 내 외과 간호학 실습만으로 소아과 간호학 실습을 위한 충분한 준비가 되지 못한다는 것을 뜻 할 수도 있다. 한편, 문헌연구에서 밝힌바와 같이 어리고 연약해 보이는 어린이들의 신체조건이 학생들의 간호활동을 어렵게 하는 경우도 될 수 있겠다. 2. 간호학생들의 어린이 환자와의 대화나 원만한 인간관계에서의 긴장감은 이 연구결과로 평가나 제언이 힘들다. 조사결과에서 학생들은 주로 어린이의 상태가 좋지 않은 경우에 심한 긴장감을 가졌고 일반적인 간호의 경우에는 별로 긴장감이 없었다. 이것은 질병의 상태나 화제, 이야기 할 때의 상황에 따라 긴장감의 여부가 달라질 수 있다는 결론이 되겠다. 3. 소아병실에서의 다양한 역활을 수행하는 것은 비교적 긴장감이 많이 생기는 간호활동으로 나타났다. 그러나 재미있는 사실은 학생들이 간호원으로서의 전문가적인 입장에서, 환자나 보호자를 가르칠때는 별로 문제가 없었으나 어린이 기르는 방법이나 어린이 이해면에서 좀 더 잘 안다고 생각되는 어머니가 지켜 볼때의 어린이 간호에는 긴장감을 가진다는 사실이다. 이것은 Jewett의 연구에서 밝힌바와는 상반되는 결과다. 그의 연구에서 학설들은 부모나 어머니들에 의해 전문가로서 인정받고 기대되는 경우가 제일 어려운 경험이 있다고 밝혔다. 4. 어린이 환자나 그들 부모의 간호에 대한 의뢰심은 학생들에게 심한 긴장감을 주는 경험이 있다. 특히 신체적 간호에 대해 의뢰하는 경우에는 더 심한 긴장감을 준다고 표현한다. 일반적으로 부모가 병실에 상주하는 경우에는 그들의 의뢰심이 심하며 이것은 학생들에게 감당하기 힘든 긴장과 어려움을 주게된다. 왜냐하면 성년기의 학생들은 그들 자신이 먼저 타인에게서 이해받기를 원하며 또 관용을 베풀어주기를 원하는데 그것을 남에게 주어야 하는 입장은 학생들을 긴장되게 하는 실습활동인 것이다. 5. 학생들은 그들이 배운 간호의 이상이나 가치가 실습지에서의 여러 경우와 맞지 않는 것을 보았을 때 극심한 긴장감을 갖는다고 밝혔다. 의사나 병원 행정의 사실이 자신의 이상과 맞지 않는다는 것보다는 간호원의 간호업무의 차이에서 더 비판적인 반응을 보였다. 대부분의 성년기 학생들은 그들의 이상적인 간호원상을 그들의 선배나 실무 간호원 중에서 찾으려는 시기에 그들의 간호활동이 이론과 다른 점이나 학생 자신의 소아과 간호의 가치와 다른 것을 보았을 때는 심한 반감과 긴장감을 갖게된다. 이 문제는 어린 사람이 윗 어른과 함께 동료의식을 갖고 일하기 어려운 한국적 사회구조 때문에 더 심하게 긴장감을 주는 경험인지도 모른다. 선배 간호원의 전문인으로서의 권위와 어린이 환자 보호자의 어른으로서의 권위 사이에서 자신의 이상과 가치의 추구는 용이하지 않으며 내적 갈등은 어쩔수 없는 일 일 것이다. 6. 대부분 높은 백분율의 긴장 반응은 죽음이나 환자의 사망에 관련된 간호활동 항목에서 나타났다. 이 연구의 대상 학생들은 2, 3학년에서 죽음에 대한 강의를 들었지만 이 연구 결과에 의하면 충분한 학습 경험이 주어진 것 같지 않다. 어떠한 경우의 죽음에라도 어린이 환자나 그 보호자들의 심리를 잘 이해하고 반응을 잘 관찰해서 적절한 간호를 해 줄 수 있는 다양한 방법의 학습 경험이 필요하다고 보겠다. 7. 학생들의 긴장도가 어린이 간호에 더 심한가, 보호자 간호에 더 심한가를 알기 위한 비교 결과는 비교적 비슷한 정도로 나타났다. 8. 학생들의 소아과 간호학 실습시의 긴장도는 과거의 병실 실습기간의 장, 단이나 가정에서의 어린인 간호의 경험과는 별 연관성이 없었다. 연구의 대상자가 적기 때문에 단정을 하기는 힘이 들지만 소아과 간호학 실습이 다른 병실의 실습과는 분리되어서 완전히 다르게 다루어 져야만 하며 간호교육자들의 주의 깊은 관심과 노력이 필요한 실습교육이 라 하겠다. 이상의 전반적인 고찰에 의하면 한국 성년기 간호학생들의 소아과 간호학 실습은 미국의 경우와 마찬가지로 긴장감을 주는 경험이다. 문화배경의 다른 점은 무시하고라도 Davis와 Oleson이 결론한 바와 같이 "간호대 학생은 어디를 막론하고 다 같은 성격과 문제를 가지고 있다. " 앞으로 보다 효과적인 학생들의 소아과 간호학 실습을 위한 연구를 위해 다음의 몇 가지를 본 연구의 결과를 가지고 제언한다. 1. 보다 여러 지역에서 다양한 교육 방법을 가진 학교의 학생을 대상으로 한 연구의 필요성. 2. 학생들이 실습 전 선입견이나 이미 들어서 생긴 긴장감의 개입 여부를 밝힐 수 있는 연구. 3. 학생 개인의 과거 경침이 긴장감 유발에 미치는 영향을 위한 연구 4. 이 연구의 결과를 입증할 수 있는 종단적 연구. 5. 이 연구에서 나타난 긴장감이 학습 행위에 미치는 영향을 알기 위한 관찰적 연구. 이 연구를 위해 많은 도움을 주셨던 보스턴대학의 Dr. Kennedy와 연세대학의 여러 선생님께 심심한 감사를 드립니다.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (