• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.116-125
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• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

• Journal of IKEEE
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• v.21 no.1
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• pp.13-23
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• 2017

Short-term prediction of the number of passengers at the airport is very essential for the efficient and stable operation of the airport. Here, to forecast the immigration of Incheon International Airport, we perform the predictive modeling of Korean and Chinese outbound travelers comprising most of immigration. We conduct the Granger Causality test between the number of outbound travelers and related search trend data to confirm the correlation. It is found that the forecasting with both "outbound travelers" and "search term trends" data outperforms the one only with "outbound travelers" data. This is because search activities are done before doing something and this study confirms that search trend data inherently possess the potential for prediction.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.243-252
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• 2014

Many researches about the effect on politics, economics and Sociocultural phenomenon using the social media are in progress. Authors utilized NAVER Trend most famous web browsing service in korea, NAVER Blog social media, NAVER Cafe service and Open Data(API) and also used temperature, humidity index data of Korea Meteorological Administration. This study analyzed a change of the public's emotion in korea using Cluster analysis of vocabulary of taste among its of feelings and senses. K-means clustering was followed by decision of the number of groups which was used Chi-square goodness of fit test and ward analysis. Eight groups was made and it represented sensitive vocabulary. By Discriminant analysis, eight groups decided by Cluster analysis has 98.9% accuracy. The change of the public's emotion has capability to predict people's activity so they can share sensibility and a bond of sympathy developed between them.

• The Journal of Bigdata
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• v.3 no.1
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• pp.63-71
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• 2018

In the last five years (2013~2017), we analyzed what factors influenced Korean films that have surpassed 10 million viewers in the Korean movie industry, where the total number of moviegoers is over 200 million. In general, many people consider the number of screens and ratings as important factors that affect the audience's success. In this study, four additional factors, including the number of screens and ratings, were established to establish a hypothesis and correlate it with the presence of 10 million spectators through big data analysis. The results were significant, with 91 percent accuracy in predicting 10 million viewers and 99.4 percent accuracy in estimating cumulative attendance.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.158-161
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• 2012

본 연구는 뉴스레터는 자사의 소식이나 정보를 제공한다. 그리고 고객은 거기에서 필요한 정보를 받고 거기에 반응을 하므로 정보를 효과적으로 고객에게 신속하고 정확하게 보내는 것은 중요하다. 정보의 송신자(Source)로부터 수신자에게 채널(Channel)을 통하여 전달된다. 채널을 이용하기 위해서 송신자는 채널에 알맞은 기호로 송신자의 의도를 바꾸어야 한다. 2012여수세계박람회 뉴스레터가 주요하게 전달하고자 한 내용은 지난 호 보기 링크, 엑스포사이트 링크, BIE사이트 링크, 입장권 예매 싸이트 링크, 참가국 현황 링크, 유튜브 동영상 링크, 남도 바로가기 링크, 알려드립니다 바로가기 링크, 네이버 블로그 이벤트 링크 등이다. 뉴스레터의 시각적 측면에서 이미지의 트렌드, 레이아웃의 정렬, 일러스트레이션, 사진, 카피 등의 조화와 정보적 측면에서 링크의 통일성, 경로 예측, 언어적 접근성을 분석하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.421-422
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• 2022

코로나19의 영향으로 개인 여가 시간이 증가하면서 성격 유형 심리 테스트들에 대한 관심도가 늘어나고 하나의 유행처럼 다양한 종류의 심리 테스트들이 등장하였다. 즉 앞으로도 심리 테스트에 대한 수요는 높을 것이라고 예상되기 때문에 트렌드에 따라가보자 해당 연구를 진행하게 되었다. 이 연구에는 JS를 기본적으로 사용했지만 추가적으로 Node.js를 이용하여 테스트 피드백을 작성하고 작성한 데이터를 백엔드로 가져오는 작업을 수행했다. 또한 Bootstrap으로 전체적인 디자인을 담당하고 테스트 결과 공유를 위해 카카오톡, 페이스북, 네이버, 트위터의 API를 가져와 사용했다. 해당 테스트를 이용할 시 심리 테스트뿐만 아니라 테스트에 대한 피드백을 관리자에게 보낼 수 있고 테스트 전체 결과 페이지를 통해 원하는 결과까지 알 수 있어 테스트 결과에만 중점을 둔 일회성 강한 다른 테스트들과는 다른 차별점을 가지고 있어 경쟁력이 있는 프로젝트라고 볼 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.15-18
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• 2020

특정 영화의 스크린 독과점이 꾸준히 논란이 되고 있다. 본 논문에서는 영화 스크린 분배의 불평등성을 지적하고 이에 대한 개선을 요구할 근거로 머신러닝 기법을 활용한 영화 관람객 예측 모델을 제안한다. 이에 따라 KOBIS, 네이버 영화, 트위터, 구글 트렌드에서 수집한 3,143개의 영화 데이터를 이용하여 랜덤포레스트와 그라디언트 부스팅 기법을 활용한 영화 관람객 예측 모델을 구현하였다. 모델 평가 결과, 그라디언트 부스팅 모델의 RMSE는 600,486, 랜덤포레스트 모델의 RMSE는 518,989로 랜덤포레스트 모델의 예측력이 더 높았다. 예측력이 높았던 랜덤포레스트 모델을 활용, 상영관을 크게 확보하지 못 했던 봉준호 감독의 영화 '옥자'의 상영관 수를 조절하여 관람객 수를 예측, 6,345,011명이라는 결과를 제시한다.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• Land and Housing Review
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• v.13 no.4
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• pp.45-53
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• 2022

This paper investigates empirically the lead-lag relation between the 'apartment price index' and 'Internet search volume'. This study uses Naver Trend Index as a proxy for Internet search volume. An increase in Internet search volume on the apartment price index indicates an increase in people's attention to an apartment. Different from previous studies exploring the relation between 'the released price index of the apartment' and 'Naver Trend Index', this study investigates the relation of the Naver Trend Index with 'the fundamental price component of an apartment' and 'the transitory price component of an apartment', respectively. The results of the Granger causality test reveal that there are bidirectional Granger causalities between the 'released price' and Naver Trend Index. In addition, the 'fundamental price component of an apartment' and Naver Trend Index have a feedback relation, while 'the transitory price component of an apartment' Granger causes the Naver Trend Index uni-directionally. The impulse response function analysis indicates that the shock of apartment prices increases Naver Trend Index in the first month. Overall, The close relationship between apartment prices and Naver Trend Index suggests that increases in the movement of apartment prices are positively associated with public attention on the apartment market.

• The Korea Journal of Herbology
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• v.36 no.5
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• pp.1-14
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• 2021

Objectives : From January 2020, when the first confirmed case of COVID-19 in Korea, the use of health information using the Internet is expected to increase. It is expected that there will be a significant change in the general public's interest in Korean herbal medicines for health care. Therefore, in this study, we tried to confirm the change in the search trend of Korean herbal medicines after the COVID-19 epidemic. Methods : Using the "Naver DataLab (http://datalab.naver.com)" service of a Korean portal site Naver, search volume was investigated with 606 Korean herbal medicines as keywords. The search period was from January 2020, right after the onset of COVID-19, to June 2021. The search results were sorted by the peak search volume and the total search volume. Results : 'Cheonsangap (천산갑, 穿山甲, Manitis Squama)' was the most searched Korean herbal medicine in the peak search volume and total search volume with least bias. Conclusions : The problem of supply and demand of Korean herbal medicines of high public interest was identified. Broadcasting and media exposure were the factors that had a big impact on the search volume for Korean herbal medicines. As it was confirmed that the search volume for Korean herbal medicines increased rapidly due to media exposure, it is necessary to provide correct information about Korean herbal medicines, improve public awareness, and manage stable supply and demand based on continuous search trend monitoring.