• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.639-653
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• 2020

Baker et al. (Quarterly Journal of Economics, 134, 1593-1636, 2016) developed an Economic Policy Uncertainty (EPU) index for South Korea in the same way as the U.S. EPU Index. However, the South Korean EPU index of Baker et al. (2016) has limitations as it did not fully reflect South Korean situation in terms of keyword selection and the selection of newspapers. We develop monthly South Korean economic policy uncertainty indexes with different keywords and news media. Various analyses have been conducted in order to examine the usefulness of the newly compiled indexes.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.187-197
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• 2020

Recently, various methods of text embedding using deep learning algorithms have been proposed. Especially, the way of using pre-trained language model which uses tremendous amount of text data in training is mainly applied for embedding new text data. However, traditional pre-trained language model has some limitations that it is hard to understand unique context of new text data when the text has too many tokens. In this paper, we propose self-supervised learning-based fine tuning method for pre-trained language model to infer vectors of long-text. Also, we applied our method to news articles and classified them into categories and compared classification accuracy with traditional models. As a result, it was confirmed that the vector generated by the proposed model more accurately expresses the inherent characteristics of the document than the vectors generated by the traditional models.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.115-124
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• 2004

The content-based retrieval using low-level features can hardly provide the retrieval result that corresponds with conceptual demand of user for intelligent retrieval. Video includes not only moving picture data, but also audio or closed-caption data. Knowledge-based video retrieval is able to provide the retrieval result that corresponds with conceptual demand of user because of performing automatic indexing with such a variety data. In this paper, we present the knowledge-based video retrieval system using Korean closed-caption. The closed-caption is indexed by Korean keyword extraction system including the morphological analysis process. As a result, we are able to retrieve the video by using keyword from the indexing database. In the experiment, we have applied the proposed method to news video with closed-caption generated by Korean stenographic system, and have empirically confirmed that the proposed method provides the retrieval result that corresponds with more meaningful conceptual demand of user.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.585-592
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• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6411-6418
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• 2015

To respond the climate change and environmental pollution, the studies on renewable energy policies are increasing. The renewable energy is a new growth engine technology represented by the green industry and green technology. At present, the investments for the renewable energy supply and technology development projects of three main strategy sectors such as sunlight, wind power and hydrogen fuel cell are implemented in our country, while they are still in the early stage, accordingly reducing those uncertainty for the research direction and investment fields is the most urgent issue among others. Thus, this study applied text mining method and multinominal topic model among the big data analysis methods on our country's newspaper articles concerning the renewable energy over the last 10 years, and then analyzed the core issues and global research trend, forecasting the renewable energy fields with the growth potential. It is predicted that these results of the study based on information and communication technology will be actively applied on the renewable energy fields.

• Journal of Information Science Theory and Practice
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• v.11 no.1
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• pp.14-30
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• 2023

The purpose of this study is to compare research trends in the information and communication technology (ICT) field between North and South Korea and analyze the differences by using data mining. Frequency analysis, clustering, and network analysis were performed using keywords from seven South Korean and two North Korean ICT academic journals published for five years (2015-2019). In the case of South Korea (S. Korea), the frequency of research on image processing and wireless communication was high at 16.7% and 16.3%, respectively. North Korea (N. Korea) had a high frequency of research, in the order of 18.2% for image processing, 16.9% for computer/Internet applications/security, and 16.4% for industrial technology. N. Korea's natural language processing (NLP) sector was 11.9%, far higher than S. Korea's 0.7 percent. Student education is a unique subject that is not clustered in S. Korea. In order to promote exchanges between the two Koreas in the ICT field, the following specific policies are proposed. Joint research will be easily possible in the image processing sector, with the highest research rate in both Koreas. Technical cooperation of medical images is required. If S. Korea's high-quality image source is provided free of charge to N. Korea, research materials can be enriched. In the field of NLP, it calls for proposing exchanges such as holding a Korean language information conference, developing a Korean computer operating system. The field of student education encourages support for remote education contents and management know-how, as well as joint research on student remote evaluation.

• Journal of Korean Society on Water Environment
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• v.40 no.2
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• pp.89-100
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• 2024

Climate change has a broad impact on the entire water environment, and this impact is growing. Climate adaptation in water supply systems often involves quantity and quality control, but there has been a lack of research examining the impacts of climatic factors on water supply productivity and operation conditions. Therefore, the present study focused on, first, building a database of climatic factors and water purification operating conditions, and then identifying the correlations between factors to reveal their impacts. News big data was analyzed with keywords of climatic factors and water supply systems in either nationwide or region-wide analyses. Metropolitan area exhibited more issues with cold waves whereas there were more issues with drought in the Southern Chungcheong area. A survey was conducted to seek experts' opinions on the climatic impacts leading to these effects. Pre-chlorination due to drought, high-turbidity of intake water due to rainfall, an increase of toxins in intake water due to heat waves, and low water temperature due to cold waves were expected. Pearson correlation analysis was conducted based on meteorological data and the operating data of a water purification plant. Heavy rain resulted in 13 days of high turbidity, and the subsequent low turbidity conditions required 3 days of high coagulant dosage. This insight is expected to help inform the design of operation manuals for waterworks in response to climate change.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.724-732
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• 2023

In this study, we tried to analyze the characteristics of color cosmetics information search and the major information of interest in the color cosmetics market after COVID-19 shown in the text mining analysis results by collecting data on online interest information of consumers in the color cosmetics market after COVID-19. In the empirical analysis, text mining was performed on all documents such as news, blogs, cafes, and web pages, including the word "color cosmetics". As a result of the analysis, online information searches for color cosmetics after COVID-19 were mainly focused on purchase information, information on skin and mask-related makeup methods, and major topics such as interest brands and event information. As a result, post-COVID-19 color cosmetics buyers will become more sensitive to purchase information such as product value, safety, price benefits, and store information through active online information search, so a response strategy is required.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.391-396
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• 2024

This study proposes a novel reverse N-Gram approach to overcome the limitations of traditional N-Gram methods and enhance performance in building an entity dictionary specialized for the healthcare sector. The proposed reverse N-Gram technique allows for more precise analysis and processing of the complex linguistic features of healthcare-related big data. To verify the efficiency of the proposed method, big data on healthcare and digital health announced during the Consumer Electronics Show (CES) held each January was collected. Using the Python programming language, 2,185 news titles and summaries mentioned from January 1 to 31 in 2010 and from January 1 to 31 in 2024 were preprocessed with the new reverse N-Gram method. This resulted in the stable construction of a dictionary for natural language processing in the healthcare field.