• The Journal of Information Systems
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• v.17 no.3
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• pp.235-254
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• 2008

This article describes the latest development of a generic approach to detecting emerging trends and transient in scientific literature. The work makes substantial theoretical and methodological contributions to progressive Information analysis. A specialty is conceptualized as a time variant duality research front concepts in information science. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The contributions of the approach is that the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms. The modeling process is implemented in RADERS, and applied to the analysis of telecommunication field. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.236-243
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• 2009

With advances in topic detection and tracking(TDT), automatic trend analysis from a collection of time-stamped documents, like patents, news papers, and blog pages, is a challenging research problem. Past research in this area has mainly focused on showing a trend line over time of a given concept by measuring the strength of trend-associated term frequency information. for detection of emerging trends, either a simple criterion such as frequency change was used, or an overall comparison was made against a training data. We note that in order to show most salient trends detected among many possibilities, it is critical to devise a ranking function. To this end, we define four properties(change, persistency, stability and volume) of trend lines drawn from frequency information, to quantify various aspects of trends, and propose a method by which trend lines can be ranked. The properties are examined individually and in combination in a series of experiments for their validity using the ranking algorithm. The results show that a judicious combination of the four properties is a better indicator for salient trends than any single criterion used in the past for ranking or detecting emerging trends.

• The Journal of Information Systems
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• v.18 no.4
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• pp.19-40
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• 2009

승자독식(Winner Takes All)이라는 글로벌 경쟁원리로 인해 세계 주요 국가들 사이에 R&D 경쟁이 갈수록 치열해지고 있다. 이에 우리나라에서도 R&D 지원 확대 및 다양한 정책기획 활동 등이 전개되었다. 이러한 노력에도 불구하구 국가 차원에서 기획 및 정책조정이 효과적으로 추진되지 못하고 있으며, 국가연구개발사업 차원에서는 "기획의 양척 과다와 질적 부실"이라는 비판이 제기되고 있다. 반면에 기술 융복합화의 가속화 등 R&D 환경의 불확실성은 더욱 높아졌으며 국가연구개발사업의 규모 증가와 함께 연구기획의 복잡성은 더욱 더 높아졌다. 최근에는 국가 차원의 비전수립과 중장기 정책기획에 기초하여 사업기획과 과제기획을 연계 수행하는 등 기획을 통해 과제를 도출하는 하향식 접근이 강조되면서 기획의 중요성은 더욱 커졌다. 최근 연구기획의 새로운 기법으로서 정보분석 방법론(Information Analysis Method)이 대두되고 있다. 국가연구개발사업의 효율적 기획을 위하여 기존 기술기획 위원회(Peer Review) 방식 외에 정보분석 방법론을 통한 보완이 필요하다. 본고의 목적 크게 두 가지로 설명할 수 있다. 첫째, 현재 진행되고 있는 연구기획 및 연구기획 방법론으로 기장 많이 활용되고 있는 전문가 위원회의 한계를 제시하였다. 둘째, 최근 대두되고 있는 정보분석 방법론과 정보분석 시스템 구축, 그리고 이를 활용한 10대 기술(반도체, 디스플레이, 디지털 망원전파, 이동통신, BcN, 차세대 컴퓨팅, SW솔류션, 디지털콘텐츠, 임베디드 SW, 지식정보보안)에 대한 유망기술 발굴 실증분석을 실시하였다.

• Electronics and Telecommunications Trends
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• v.31 no.2
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• pp.18-27
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• 2016

국가 경제의 지속적인 발전이 가능하게 하려면 미래 유망기술의 발굴이 중요함은 널리 알려져 있다. 하지만, 과학기술의 융 복합화와 불확실성의 증가 그리고 기존 기술의 틀을 깨는 혁신적 기술의 등장 등으로 인해서 실효성 높은 미래 유망기술의 발굴이 어려워지고 있다. 미래 유망기술 선점을 위해서는 fast follower 전략에서 first mover 형으로 전환이 필요하며, 이를 위해서는 기술태동 초기의 weak signal을 파악해야 한다. 지금까지 weak signal 탐지는 주로 전문가 기반으로 이루어져 왔으나 분석 시간이 오래 걸릴 뿐만 아니라 대상 분야가 넓어지고 분석할 데이터의 양이 급격히 증가하면서 정량적 데이터 분석을 보완적으로 사용하는 방향으로 패러다임의 변화가 일어나고 있다. 하지만, 텍스트 분석을 통한 weak signal 탐지기술은 아직 기초적인 수준에 머물러 있어서 관련 연구에 대한 투자가 필요하다.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.515-523
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• 2011

It is important to manage large amount of information from various information providers for supporting recent information services such as providing global research trends, detecting emerging signal and listing leading researchers. For integrated management, definition of integrated metadata schema, data transformation and schema matching are needed. It is first necessary to analyze existing various metadata for defining integrated metadata schema. In this paper, we have analyzed several metadata of scientific journal papers by classifying semantics, content rules and syntax, and looked around considerations to make integrated schema or transform metadata. We have known that XML is used as a syntax for supporting convenience and various usage condition, and hierarchy element names and common elements in semantics are needed. We also have looked at elements having various content rules and related standards. We hope that this study will be used as basic research material of metadata integrated management, data transform and schema matching for interoperability.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.