• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.343-349
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• 2022

We intend to use topic modeling to identify radiation-themed papers published from 1989 to 2022 and analyze the relevance and weight between topics. This study analyzed topics derived from national subjects for 717 papers published until recently in 2022 to contribute to the revitalization of research in the field of radiation. Through text mining, overall research trends on the subject distribution of the study were analyzed, and five topics were derived through topic modeling. First, among the papers to be analyzed, a total of 1,675 words were frequency-analyzed through the preprocessing process of key words in a total of 717 papers centered on keywords. Second, as a result of analyzing topics based on the association of constituent words for five topics, it was found that studies focused on minimizing dose in the range that does not degrade image quality in the fields of radiation, image, CT clinical. In addition, it was found that various studies were mainly conducted in the MRI, and the study of ultrasound in various areas of disease analysis was actively attempted.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.139-150
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• 2020

The data processing of this study focuses on the textom and social media words about three areas: 'Corona 19 and professional baseball', 'Corona 19 and professional baseball', and 'Corona 19 and professional sports' The data was collected and refined in a web environment and then processed in batch, and the Ucinet6 program was used to visualize it. Specifically, the web environment was collected using Naver, Daum, and Google's channels, and was summarized into 30 words through expert meetings among the extracted words and used in the final study. 30 extracted words were visualized through a matrix, and a CONCOR analysis was performed to identify clusters of similarity and commonality of words. As a result of analysis, the clusters related to Corona 19 and Pro Baseball were composed of one central cluster and five peripheral clusters, and it was found that the contents related to the opening of professional baseball according to the corona 19 wave were mainly searched. The cluster related to Corona 19 and unrelated to professional baseball consisted of one central cluster and five peripheral clusters, and it was found that the keyword of the position of professional baseball related to the professional baseball game according to Corona 19 was mainly searched. Corona 19 and the cluster related to professional sports consisted of one central cluster and five peripheral clusters, and it was found that the keywords related to the start of professional sports according to the aftermath of Corona 19 were mainly searched.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.145-158
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• 2020

Because individuals come together to form a society, society has characteristics that are different or similar. Furthermore, as globalization and language acquisition in other countries have been activated, the frequency of studying abroad has increased. While Korea also studies abroad, the number of students coming to Korea from other countries continues to increase, increasing. In particular, when there was interest in start-up businesses, the factors were discovered through exploratory research in order to identify factors affecting the level of growth in start-ups when they had different nationalities. In order to conduct exploratory research, the government wanted to check more in-depth information through semi-structured interviews with co-founder companies composed of Koreans and Chinese. The main keywords were repeated or emphasized continuously during the interview, and other keywords were obtained through additional questions. As a result, it has been confirmed that self-acceptance, cultural distance, entrepreneurship, knowledge quality and growth are very large keywords in the co-founding start-up of different countries. The proposition was established as having a relationship of justice with self-acceptance, cultural distance and entrepreneurship as independent variables and with the degree of growth as dependent variables. In particular, in the case of co-founding with different nationalities, the most important knowledge quality was represented the effect of reverse U in each relationship (the relationship between self-acceptance, cultural distance, entrepreneurship and growth).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.772-774
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• 2017

Currently, there are many simple searches for local culture and tourism, but systematic information retrieval using ontology technology is weak. The keyword-based search, which is an existing search method, derives a search result that is different from a user's wanted intention. On the other hand, semantic search using ontology constructs shows the information related to the search term by creating a relation between words and words. Therefore, when tourists search for cultural and tourist attractions in the area, they provide information that includes meaning relevance in the search results. If the ontology provides information on the culture, sightseeing area, transportation, Can be more easily grasped. In this paper, we propose an ontology-based retrieval system based on culture and tourist sites utilizing public institutions database by using mobile application by extending search system which relied only on existing internal database to provide accurate and reliable information to users. This efficient structure of the ontology makes it possible to provide information suitable for the user quickly and accurately.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.89-96
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• 2006

In this paper, we propose two approaches for the character segmentation on Korean document images. One is an improved version of a projection profile-based algorithm. It involves estimating the number of characters, obtaining the split points and then searching for each character's boundary, and selecting the best segmentation result. The other is developed for low quality document images where adjacent characters are connected. In this case, parts of the projection profile are cut to resolve the connection between the characters. This is called ${\alpha}$-cut. Afterwards, the revised former segmentation procedure is conducted. The two approaches have been tested with 43,572 low-quality Korean word images punted in various font styles. The segmentation accuracies of the former and the latter are 91.81% and 99.57%, respectively. This result shows that the proposed algorithm using a ${\alpha}$-cut is effective for low-quality Korean document images.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.239-259
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• 2022

Since 2019, research on microplastics has been actively conducted around the world, so analyzing the differences between domestic and foreign microplastics research can be a milestone in establishing the direction of domestic research. In this study, microplastic papers from KCI and WoS were extracted and the differences between domestic and foreign studies were analyzed using a network analysis methodology based on big data such as author keyword co-occurrence word analysis, thesis co-citation analysis, and author co-citation analysis. As a result of the analysis, the analysis of the research topic confirmed that studies that could affect the human body and the treatment of microplastics in daily life were additionally needed in Korea. In the analysis of the depth of thesis citation that examines the quality of research, it was found that Korea was still insufficient at 2.25 overseas and 1.39 in Korea. In the analysis of the composition of the joint research front, where various researchers participate and share information, 3 out of 22 clusters in Korea are Star type. In the case of overseas, all 19 clusters have a mesh structure, so it was confirmed that information flow and sharing were insufficient in specific research fields in Korea. These research results confirmed the need to expand the research topic of microplastics, improve the quality of research, and improve the research promotion system in which various researchers participate. In addition, if the automation program is developed based on topic modeling, it will be possible to build a system capable of real-time analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5664-5672
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• 2015

The purpose of this paper is to draw main characteristics of local climate change plans of 8 pilot cities through a serious of comparative analyses. The results of the analyses on the mitigation plans and the adaptation plans are the followings; Firstly, climate change plans have two types of distinctive frameworks. Secondly, vision, objectives, and main strategies are composed of main key words. The key words of mitigation plans are low carbon, GHG, green city, energy, and green growth. Adaptation, ecosystem, healthy, safe, disaster, water are the key words that frequently shown in adaptation plan. Thirdly, the mitigation plans tend to place emphasis on transportation and common area. The adaptation plans tend to weigh on water control and forestry. The main characteristics of both mitigation plans and adaptation plans of 8 pilot cities are summarized and policy implications are suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.633-640
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• 2021

This study analyzes the effects of text and image presenting methods in video lectures on students' learning flow, learning satisfaction and learning outcomes. The text presenting methods include forming short sentences of 2 or 3 words or using key words, while image presenting methods include images featuring both detailed and related information as well as images containing only related information. 167 first year students from Xingtai University were selected as experimental participants. Groups of participants were randomly assigned to engage in four types of video. The research results are as follows. First, it was found that learning flow, learning satisfaction and learning outcomes of group presented with video forms of short sentences had higher statistical significance compared to the group experiencing the key word method. Second, learning flow, learning satisfaction and learning outcomes of group presented with video forms of only related information had higher statistical significance compared to the group experiencing the presenting method of both detailed and related information. That is, the mean values of dependent variables for groups of short form text and only related information were highest. In contrast, the mean values of dependent variables for groups of key words and both detailed and related information were the lowest.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.10-20
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• 2007

This paper describes a concept-based question analysis to analyze concept which is more important than keyword for the accurate answer extraction. Our idea is that we can extract correct answers from various paragraphs with different structures when we use well-defined concepts because concepts occurred in questions of same answer type are similar. That is, we will analyze the syntactic and semantic role of each word or phrase in a question in order to extract more relevant documents and more accurate answer in them. For each answer type, we define a concept frame which is composed of concepts commonly occurred in that type of questions and analyze user's question by filling a concept frame with a word or phrase. Empirical results show that our concept-based question analysis can extract more accurate answer than any other conventional approach. Also, concept-based approach has additional merits that it is language universal model, and can be combined with arbitrary conventional approaches.