• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.309-317
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• 2023

The purpose of this paper is to investigate how Japan tried to dominate Chosen through educational policies by analyzing three official English texts published by the Japanese Government-General of Korea: the First Chosen Educational Ordinance declared in 1911, the Manual of Education of Koreans(1913), and the Manual of Education in Chosen 1920(1920). In order to pursue this purpose, the present study carried a corpus-based diachronic analysis, rather then a qualitative analysis. Facilitating text analytics such as Word Cloud and CONCOR, this paper derived the following results: First, the first Chosen Educational Ordinance(1911) includes overall educational regulations, curriculum, and operations of schools. Second, the Manual of Education of Koreans(1913) contains the educational medium and contents on how to educate. Finally, it can be proposed that the Manual of Education in Chosen 1920(1920) contains specific implementation of education and the subject of education.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.360-371
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• 2021

The advent of virtual currencies has heightened interest in the Central Bank Digital Currency (CBDC) issued by the central bank. Recently, central banks in some countries have already decided to issue CBDCs or are in the test phase. This study will be an opportunity to compare public and private perceptions of central banks and explore various issues related to the introduction of CBDCs in the future through analysis methods of big data.

• Journal of Fashion Business
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• v.27 no.4
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• pp.50-66
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• 2023

The purpose of this study was to examine the word distribution and topic distribution of slow fashion appearing on YouTube in detail and identify the characteristics and aspects related to fashion design through big data analysis and content analysis methods. The specific research results were as follows. First, in the results of the word distribution analysis, "item" appeared the most, 203 times. Also, "one-piece" was a point to pay attention to, as the item had the highest frequency. Second, a total of 5 topics were defined in the topic distribution analysis: topic 1 was "vintage products," topic 2 was "fashion items," topic 3 was "eco-friendly," topic 4 was "life quality emphasis," and topic 5 was "prudent consumption." Third, looking at the relationship between word distribution and topic distribution above, Korean slow fashion on YouTube was actively selecting related design elements that express vintage images in clothing life regardless of trends. In addition, there was a tendency to pursue various basic and high-quality items. Other than those findings, basic items tended to be reinterpreted in various ways through styling methods matched to the vintage image. Lastly, the tendency of slow and small-volume production appeared to emphasize handicrafts and the cultural values of fashion products.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.368-369
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• 2023

대화 데이터 기반 광고 추천은 광고 마케팅에서 고객 맞춤형 광고 제공, 마케팅 효과 극대화 등을 위한 중요한 기술로 주목받고 있다. 본 논문에서는 모바일 인스턴스 메신저인 카카오톡 대화창에서 발생한 텍스트 데이터를 기반으로 대화 내용을 분석하여 대화 주제별 적절한 광고 키워드를 제안한다. 이를 위해 주제별 대화 내용을 미용, 식음료, 상거래로 세분하고 KoNLPy 의 Okt 를 이용하여 텍스트 전처리를 수행하고 키워드별로 빈도수를 뽑아 워드 클라우드를 제시한다. 또한, 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA)을 기반으로 대화 주제를 세분화한 뒤 라벨링을 통해 주제별 대화 키워드를 분석한다. 실험 결과, 대화 주제를 온라인 쇼핑, 헤어, 뷰티 관리, 음식으로 나눌 수 있었으며, 토픽별 상위 키워드를 Word2Vec 을 통해 특정 단어와 유사한 키워드를 도출하여 적절한 광고 키워드를 제시할 수 있었다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.859-865
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• 2023

In a situation where service providers are increasingly focusing on content development due to the intense competition in the OTT market, interactive content that encourages active participation from viewers is garnering significant attention. In response to this trend, research on interactive content is being conducted more actively. This study aims to analyze interactive content through text mining techniques, with a specific focus on online unstructured data. The analysis includes deriving the characteristics of keywords according to their weight, examining the relationship between OTT platforms and interactive content, and tracking changes in the trends of interactive content based on objective data. To conduct this analysis, detailed techniques such as 'Word Cloud', 'Relationship Analysis', and 'Keyword Trend' are used, and the study also aims to derive meaningful implications from these analyses.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.4
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• pp.217-234
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• 2019

National Records Designation System is designed to protect valuable civilian records from loss or damage. It also intends that government administrates important civilian records to raise public concerns civilian records and to foster archival culture in Korea. This study investigates the current states of service fo the designated record through the web page of National Archive of Korea. Major findings are as follows. First, the information of designated records is dispersed in two web pages by the National Archive of Korea, an introductive web page of every collection in the National Archive of Korea and a web page of designated record service. Second, the web page of designated record service provides information of designated records only at collection level, so it is not easy for users to understand the contents of the records. In order to improve the service for the designated record service of the National Archive of Korea, this study proposed the unification of dispersed web pages to provide information of the designated records consistently. It also suggested a facet based directory service and word cloud service to give access to the contents of each designated record collection. The facet based directory and word cloud service will help users to understand the designated records in more detail.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Knowledge Management Research
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• v.21 no.3
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• pp.215-228
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• 2020

This study introduces the case of a company that newly jumped into the competitive cosmetics market with a brand concept developed through big data analysis. Skin Reverse Lab, which possesses anti-aging material technology, launched a new brand in the skincare cosmetics market. Using a big data analysis program called Luminoso, SNS data was analyzed in four areas, which were consumer attitudes toward overall cosmetics, skincare products, competitors, and consumers' experiences of product use. The age groups and competitors were analyzed through the emotional analysis technique including context, which is the strength of Luminoso, and insights on consumers were derived through the related word analysis and word cloud techniques. Based on the analysis results, Logically Skin have won various awards in famous magazines and apps, and have been recognized as products that meet global trend standards. Besides, it has entered six countries including the United States and Hong Kong. The Logically Skin case is a case in which a new company entered the market with a new brand by deriving consumer insights only from external data, and it is significant as a case of applying AI-based sentiment analysis.

• Journal of the Korea Fashion and Costume Design Association
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• v.20 no.3
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• pp.49-62
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• 2018

The purpose of this study is to examine women's street fashion based on Instagram by word cloud analysis. This study is divided into items, silhouettes, colors, materials, patterns, and images that appear in women's street fashion. The results of this study are as follows: First, women's fashion-oriented Instagram accounts have a maximum of 8.6 million followers, with 16 blogs have more than one million users. As for the fashion-oriented Instagram visitors, many were their 10s-20s and photography was the key issue. Second, it was found that the casual image, which is the basis of street fashion, romantic, elegance, active sportive image, and sexy images appeared as unique images, and mixed with each other. Third, we compared the fashion characteristics of the top blogs 'fashionnova', 'fashionclimaxx2', and 'fashion.selection'. The blog 'fashionnova', utilizes sexy images and various dresses, and dresses were the characteristic points. The blog 'fashionclimaxx2' features casual images and modern office looks. The blog 'fashoin.selection' has fashion characteristics of both 'fashionnova' and 'fashionclimaxx2'.