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김현종(Hyun-Jong Kim),유승의(Seung-Eui Ryu),이철호(Chul-Ho Lee),남광우(Kwang Woo Nam) 한국산학기술학회 2020 한국산학기술학회논문지 Vol.21 No.9
행정기관은 정책 대응성을 제고하기 위해 빅데이터 분석에 관심을 기울이고 있다. 빅데이터 중 뉴스 기사는 정책이슈와 정책에 대한 여론을 파악하는데 중요한 자료로 활용될 수 있다. 한편으로 새로운 온라인 매체의 등장으로 뉴스기사의 생산은 급격히 증가하고 있어 문서 자동분류를 통해 기사를 수집할 필요가 있다. 그러나 기존 뉴스 기사의 범주와 키워드 검색방법으로는 특정 행정기관 및 부서별로 업무에 관련된 기사를 자동적으로 수집하는 것에 한계가 있었다. 또한 기존의 지도학습 기반의 분류 기법은 다량의 학습 데이터가 필요한 단점을 가지고 있다. 이에 본 연구에서는 행정부서의 업무특징을 포함한 분류사전을 활용하여 기사의 분류를 효과적으로 처리하기 위한 방법을 제안한다. 이를 위해 행정기관의 업무와 신문기사를 Word2Vec와 토픽모델링 기법으로 부서별 특징을 추출하여 분류사전을 생성하고, 행정 부서별로 신문기사를 자동분류 한 결과 71%정도의 정확도를 얻었다. 본 연구는 행정부서별 신문기사를 자동분류하기 위해 부서별 업무 특징 추출 방법과 비지도학습 기반의 자동분류 방법을 제시하였다는 학문적 · 실무적 기여점이 있다. Administrative agencies today are paying keen attention to big data analysis to improve their policy responsiveness. Of all the big data, news articles can be used to understand public opinion regarding policy and policy issues. The amount of news output has increased rapidly because of the emergence of new online media outlets, which calls for the use of automated bots or automatic document classification tools. There are, however, limits to the automatic collection of news articles related to specific agencies or departments based on the existing news article categories and keyword search queries. Thus, this paper proposes a method to process articles using classification glossaries that take into account each agency"s different work features. To this end, classification glossaries were developed by extracting the work features of different departments using Word2Vec and topic modeling techniques from news articles related to different agencies. As a result, the automatic classification of newspaper articles for each department yielded approximately 71% accuracy. This study is meaningful in making academic and practical contributions because it presents a method of extracting the work features for each department, and it is an unsupervised learning-based automatic classification method for automatically classifying news articles relevant to each agency.
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온라인 리뷰 빅데이터 분석을 통한 흰여울문화마을 관광 활성화 방안 연구
이새미 ( Lee Sae-mi ),유승의 ( Ryu Seung-eui ) 한국호텔리조트학회(구 한국호텔리조트카지노산학학회) 2020 호텔리조트연구 Vol.19 No.1
The purpose of this study is to investigate tourists' perceptions by analyzing tourism-related data among social big data, and to derive ideas for using social big data in tourism and promoting the tourism industry. To this end, 1162 tourists' online reviews were collected and analyzed by LDA, a representative topic modeling technique among text mining techniques, and extracted into 7 topics(sightseeing, emotional image 1, purpose of travel, parking discomfort, recommendation, architecture, emotional image 2). Topic 7 had the highest weight and consisted of keywords showing the emotional image of tourists. Topic 4 of the seven themes showed tourists' negative feelings about the parking problem. The meaning of this study is as follows. First, by analyzing the comments of tourists, authors suggest ideas for tourism based on the main issues. Second, text mining, which is one of the big data analysis technologies that can quantify reviews, which is unstructured data, is employed to analyze tourist opinions. Third, theoretical and practical implications are suggested based on the analysis results of tourist opinions.
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Bitcoin Price Forecasting Using Neural Decomposition and Deep Learning
Adyan Marendra Ramadhani(마렌드라),Kim Na Rang(김나랑),Lee Tai Hun(이태헌),Ryu Seung Eui(유승의) 한국산업정보학회 2018 한국산업정보학회논문지 Vol.23 No.4
Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.
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