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주가지수 방향성 예측을 위한 주제지향 감성사전 구축 방안
유은지(Eunji Yu),김유신(Yoosin Kim),김남규(Namgyu Kim),정승렬(Seung Ryul Jeong) 한국지능정보시스템학회 2013 지능정보연구 Vol.19 No.1
Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants’ opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day’s stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets publishe
추가 사전학습 기반 지식 전이를 통한 국가 R&D 전문 언어모델 구축
유은지 ( Eunji Yu ),서수민 ( Sumin Seo ),김남규 ( Namgyu Kim ) 한국지식경영학회 2021 지식경영연구 Vol.22 No.3
최근 딥러닝 기술이 빠르게 발전함에 따라 국가 R&D 분야의 방대한 텍스트 문서를 다양한 관점에서 분석하기 위한 수요가 급증하고 있다. 특히 대용량의 말뭉치에 대해 사전학습을 수행한 BERT(Bidirectional Encoder Representations from Transformers) 언어모델의 활용에 대한 관심이 높아지고 있다. 하지만 국가 R&D와 같이 고도로 전문화된 분야에서 높은 빈도로 사용되는 전문어는 기본 BERT에서 충분히 학습이 이루어지지 않은 경우가 많으며, 이는 BERT를 통한 전문 분야 문서 이해의 한계로 지적되고 있다. 따라서 본 연구에서는 최근 활발하게 연구되고 있는 추가 사전학습을 활용하여, 기본 BERT에 국가 R&D 분야 지식을 전이한 R&D KoBERT 언어모델을 구축하는 방안을 제시한다. 또한 제안 모델의 성능 평가를 위해 보건의료, 정보통신 분야의 과제 약 116,000건을 대상으로 분류 분석을 수행한 결과, 제안 모델이 순수한 KoBERT 모델에 비해 정확도 측면에서 더 높은 성능을 나타내는 것을 확인하였다. With the recent rapid development of deep learning technology, the demand for analyzing huge text documents in the national R&D field from various perspectives is rapidly increasing. In particular, interest in the application of a BERT(Bidirectional Encoder Representations from Transformers) language model that has pre-trained a large corpus is growing. However, the terminology used frequently in highly specialized fields such as national R&D are often not sufficiently learned in basic BERT. This is pointed out as a limitation of understanding documents in specialized fields through BERT. Therefore, this study proposes a method to build an R&D KoBERT language model that transfers national R&D field knowledge to basic BERT using further pre-training. In addition, in order to evaluate the performance of the proposed model, we performed classification analysis on about 116,000 R&D reports in the health care and information and communication fields. Experimental results showed that our proposed model showed higher performance in terms of accuracy compared to the pure KoBERT model.
유은지 ( Eunji-ji Yu ),박지영 ( Ji-young Kim ),김윤정 ( Yun-jung Kim ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
최근 증가하고 있는 글로벌 이슈의 해결을 위해 국제 공동연구를 통한 글로벌 협력체계 강화에 대한 중요성이 커지고 있다. 이러한 흐름에 따라 한국 정부에서도 2024년 국제 공동연구 관련 R&D 예산 규모를 약 3배 가량 늘릴 것으로 발표하였다. 이에 본 연구에서는 2018년부터 2021년 동안의 정부R&D 사업의 국제 공동연구 투자 현황을 살펴보고, 이를 향후 국제 공동연구에 대한 예산 계획을 수립하고, 국제 공동연구를 활성화하기 위한 전략 수립의 근거로 활용할 수 있을 것으로 기대한다.
HMD 기반 『화성능행도병 華城陵幸圖屛』 문화유산 MR 콘텐츠 시스템 개발
유은지 ( Eunji Yoo ),권도형 ( Dohyung Kwon ),유정민 ( Jeongmin Yu ) 한국정보처리학회 2020 한국정보처리학회 학술대회논문집 Vol.27 No.2
본 연구는 두 가지의 HMD를 기반으로 화성능행도병에 관한 문화유산 MR 콘텐츠 시스템을 제안한다. 화성능행도병 중에서 서장대야조도를 대상으로 지류와 실물모형 위에서 3D 모델을 증강하여, 역사적 정보제공과 훈련과정 체험을 가능하게 한다. 이를 통해 박물관·미술관 관람객들에게 새로운 형태의 조선 시대 기록화 콘텐츠를 제공함으로써 역사적 기록에 대한 정보를 효과적으로 전달하고, 색다른 시각적 효과와 흥미 요소를 제공할 수 있을 것이다.