• Convergence Security Journal
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• v.23 no.2
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• pp.11-25
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• 2023

Since President Biden's inauguration, significant cyberattacks have occurred several times in the United States, and cybersecurity was emphasized as a national priority. The U.S. is advancing efforts to strengthen the cybersecurity both domestically and internationally, including with allies. In particular, the Biden administration announced the National Cybersecurity Strategy in March 2023. The National Cybersecurity Strategy is the top guideline of cybersecurity and is the foundation of other cybersecurity policies. And it includes public-privates as well as international policy directions, so it is expected to affect the international order. Meanwhile, In Korea, a new administration was launched in 2022, and the revision of the National Cybersecurity Strategy is necessary. In addition, cooperation between Korea and the U.S. has recently been strengthened, and cybersecurity is being treated as a key agenda in the cooperative relationship. In this paper, we examine the cyber security strategies of the Trump and Biden administration, and analyze how the strategies have changed, their characteristics and implications in qualitative and quantitative terms. And we derive the implications of these changes for Korea's cybersecurity policy.

• Smart Media Journal
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• v.12 no.8
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• pp.9-17
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• 2023

The development of information and communication technology and the improvement of the development and dissemination of smart devices have caused changes in the form of tourism, and the concept of smart tourism has since emerged. In this regard, researches related to smart tourism has been conducted in various fields such as policy implementation and surveys, but there is a lack of research on application reviews. This study collects Trip Advisor application review data in the Google Play Store to identify usage of the application and user satisfaction through Latent Dirichlet Allocation (LDA) topic modeling. The analysis results in four topics, two of which are positive and the other two are negative. We found that users were satisfied with the application's recommendation system, but were dissatisfied when the filters they set during search were not applied or that reviews were not published after updates of the application. We suggest more categories can be added to the application to provide users with different experiences. In addition, it is expected that user satisfaction can be improved by identifying problems within the application, including the filter function, and checking the application environment and resolving the error occurring during the application usage.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.77-92
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• 2014

Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we propose a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. First, we attempt to find the relationship between documents and topics by using the result of topic analysis for single-categorized documents. Second, we construct a correspondence table between topics and categories by investigating the relationship between them. Finally, we calculate the matching scores for each document to multiple categories. The results imply that a document can be classified into a certain category if and only if the matching score is higher than the predefined threshold. For example, we can classify a certain document into three categories that have larger matching scores than the predefined threshold. The main contribution of our study is that our methodology can improve the applicability of traditional multi-category classifiers by generating multi-categorized documents from single-categorized documents. Additionally, we propose a module for verifying the accuracy of the proposed methodology. For performance evaluation, we performed intensive experiments with news articles. News articles are clearly categorized based on the theme, whereas the use of vulgar language and slang is smaller than other usual text document. We collected news articles from July 2012 to June 2013. The articles exhibit large variations in terms of the number of types of categories. This is because readers have different levels of interest in each category. Additionally, the result is also attributed to the differences in the frequency of the events in each category. In order to minimize the distortion of the result from the number of articles in different categories, we extracted 3,000 articles equally from each of the eight categories. Therefore, the total number of articles used in our experiments was 24,000. The eight categories were "IT Science," "Economy," "Society," "Life and Culture," "World," "Sports," "Entertainment," and "Politics." By using the news articles that we collected, we calculated the document/category correspondence scores by utilizing topic/category and document/topics correspondence scores. The document/category correspondence score can be said to indicate the degree of correspondence of each document to a certain category. As a result, we could present two additional categories for each of the 23,089 documents. Precision, recall, and F-score were revealed to be 0.605, 0.629, and 0.617 respectively when only the top 1 predicted category was evaluated, whereas they were revealed to be 0.838, 0.290, and 0.431 when the top 1 - 3 predicted categories were considered. It was very interesting to find a large variation between the scores of the eight categories on precision, recall, and F-score.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1458-1467
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• 2010

Recently, imitation and infringement rights of an intellectual property are being recognized as impediments to nation's industrial growth. To prevent the huge loss which comes from theses impediments, many researchers are studying protection and efficient management of an intellectual property in various ways. Especially, the prediction of patent registration is very important part to protect and assert intellectual property rights. In this study, we propose the patent document analysis method by using text mining to predict whether the patent is registered or rejected. In the first instance, the proposed method builds the database by using the word frequencies of the rejected patent documents. And comparing the builded database with another patent documents draws the similarity value between each patent document and the database. In this study, we used k-means which is partitioning clustering algorithm to select criteria value of patent rejection. In result, we found conclusion that some patent which similar to rejected patent have strong possibility of rejection. We used U.S.A patent documents about bluetooth technology, solar battery technology and display technology for experiment data.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.61-75
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• 2020

The scope of data for understanding or predicting stock prices has been continuously widened from traditional structured format data to unstructured data. This study investigates whether commentary data collected from SNS may affect future stock prices. From "Stock Discussion Room" in Naver, we collect 20 stocks' commentary data for six months, and test whether this data have prediction power with respect to one-hour ahead price direction and price range. Deep neural network such as LSTM and CNN methods are employed to model the predictive relationship. Among the 20 stocks, we find that future price direction can be predicted with higher than the accuracy of 50% in 13 stocks. Also, the future price range can be predicted with higher than the accuracy of 50% in 16 stocks. This study validate that the investors' sentiment reflected in SNS community such as Naver's "Stock Discussion Room" may affect the demand and supply of stocks, thus driving the stock prices.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.305-316
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• 2020

This study aimed to analyze the conception of an extra-solar planet perceived by university students. To conduct this, we developed an extra-solar planet education program and questionnaires which help to figure out changes between before and after the program, and then applied them to the targeted students. The results of the study are as follows. First, as to the conception of an extra-solar planet, participants understood it merely as a planet outside the solar system before they got training. However, they expanded it to the one revolving around a star that appears outside the solar system based on keywords after the training. Second, they gave brief responses regarding exploration strategies (e.g., observing the extra-solar planet by using the Doppler effect, dietary phenomenon, and gravitational lens) based on indirect experiences they encountered in the media. The responses indicated their lack of concept of the extra-solar planet exploration methods. However, their recognition of the extra-solar planet observation became concrete while students learned about the exploration of the extra-solar planet. Third, they were expanding the importance of the exoplanet observation simply beyond the discovery of extraterrestrial life to the creative process and research methods, including the solar system and the development of humanity. Fourth, they recognized that exoplanet education is necessary for curriculum as it will be able to bring about students' interest and curiosity as well as scientific knowledge if contents related to the extra-solar planet appear in the earth science curriculum.

• Knowledge Management Research
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• v.22 no.4
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• pp.217-235
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• 2021

As 49% of the world's population uses social media platforms, communication and content sharing within social media are becoming more active than ever. In this environmental base, the one-person media market grew rapidly and formed public opinion, creating a new trend called sell-sumer. This study defined new types of influencers by product category by analyzing the subject concentration of the commercial/non-commercial keywords of influencers and the impact of the ratio of commercial postings on sales. It is hoped that influencers working within social media will be helpful to new sales strategies that are transformed into sell-sumers. The method of this study classifies influencers' commercial/non-commercial posts using Python, performs text mining using KoNLPy, and calculates similarity between FastText-based words. As a result, it has been confirmed that the higher the keyword theme concentration of the influencer's commercial posting, the higher the sales. In addition, it was confirmed through the cluster analysis that the influencer types for each product category were classified into four types and that there was a significant difference between groups according to sales. In other words, the implications of this study may suggest empirical solutions of social media sales strategies for influencers working on social media and marketers who want to use them as marketing tools.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.35-44
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• 2022

In this study, we tried to confirm the characteristics of books published on the topic of international cooperation, what kind of international cooperation-related research is being conducted through this book, and what are the main contents of international cooperation. In order to achieve this research purpose, we conducted data construction, statistical analysis, and text mining based on textom in international research cooperation at home and abroad. As a result of the study, it can be seen that there has been a particularly high interest in international research and international cooperation since the 2010s. Through this, it was found that he is interested in development, economy, technology, development, region, and relations and wants to promote development. In addition, topics such as environment, trade, education, and society appeared, and interest in international research cooperation centered on environment, trade, and education was high, was found to have a high influence on society as a whole. Through this study, we find the research significance in that it can serve as a basic research to confirm the characteristics of some national and public research institutes participating in international research cooperation, and that it confirms the trend of participating in international research cooperation in a relatively specific type of institution. can see.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.