• The Science & Technology
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• v.35 no.12 s.403
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• pp.6-9
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• 2002

명왕성 밖에서 거대한 얼음-돌 천체 발견/남공 상공 오존층 작아져/가장 아름다운 물리학 실험 10가지/곤충으로 박테리아 퇴치/인공의 뇌운으로 산불 끈다/전기자동차용 리튬이온전지 개발/박테리아 위성 발사로 생명체 기원 실험/대용량 원자 메모리 기술 개발/세계에서 가장 높은 발전 타워/원자파 레이저로 화산폭발 예측/새 치아를 생물학적으로 자라게 한다/강하고 견고한 에어로겔 제조/유리 속의 분자도 구조를 가졌다

• The Science & Technology
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• v.35 no.11 s.402
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• pp.6-9
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• 2002

스웨덴 왕립아카데미 노벨상 수상자 발표/암세포에 꼬리를 단다/극초음속 순항 미사일 엔진 개발/염소 고환조직, 쥐에 이식하여 정자생성/인간의 언어 유전자 발견/도마뱀이 천장을 기어다니는 비밀은 미세한 털/공룡 단백질 재생/지구, 알려진 것보다 3천만년전 태양계 편입/ 남극에 암흑에너지 탐지용 망원경 설치/약 35억년전 거대 소행성이 지구에 충돌/말라리아 원충 모기 유전자 해독/반물질인 반수소 대량 생산

• The Science & Technology
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• v.35 no.9 s.400
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• pp.6-9
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• 2002

소아마비 바이러스 실험실 합성 성공/3억4천5백만년 전 최초의 보행동물 화석 발견/남북극에서 번개가 가장 많이 일어나/백색의 빛을 방출하는 LED개발/형상을 기억하는 플라스틱 개발/작은 입자를 걸러내는 필터/화성에서 35억년전 홍수가 대협곡을 만들었다/천연 잔디를 능가하는 새로운 인조잔디/양자 컴퓨터의 핵심 기술 실험 성공/약품의 초소형화로 흡수 효과 높인다/과일을 익히는 유전자 발견/여자가 정서적 스트레스에 의한 심장마비 잘 일으켜/생쥐 지놈의 물리지도 완성

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.59-76
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• 2023

In this study, we tried to find out port safety from various perspectives through news data that can be easily accessed by the general public and domestic academic journal data that reflects the insights of port researchers. Non-negative Matrix Factorization(NMF) based topic modeling was conducted using Python to derive the main topics for each data, and then semantic analysis was conducted for each topic. The news data mainly derived natural and environmental factors among port safety risk factors, and the academic journal data derived security factors, mechanical factors, human factors, environmental factors, and natural factors. Through this, the need for strategies to strengthen the safety of domestic ports, such as strengthening the resilience of port safety, improve safety awareness to broaden the public's view of port safety, and conduct research to develop the port industry environment into a safe and specialized mature port. As a result, this study identified the main factors to be improved and provided basic data to develop into a mature port with a port safety culture.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.153-167
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• 2019

This study aims to identify the media agenda and to compare each media agenda by media and by time period, analyzing the news about 2019 Gangwon-do's wild fire reported by 37 Korean news media. Using the topic modeling algorithm and semantic network analysis, this study inspected the configuration of the network media agenda and examined the intermedia agenda setting effect by using QAP correlation analysis. Results showed that the sensational media agenda with the attributes such as victim aid and political conflict and the similarity of each media agenda for this disaster reporting.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.315-334
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• 2013

This study performs opinion mining of newspaper articles, based on topics extracted by topic modeling. We analyze the attitudes of the news media towards a major issue of 'presidential election', assuming that newspaper partisanship is a kind of opinion. We first extract topics from a large collection of newspaper texts, and examine how the topics are distributed over the entire dataset. The structure and content of each topic are then investigated by means of network analysis. Finally we track down the chronological distribution of the topics in each of the newspapers through time serial analysis. The result reveals that both the liberal newspapers and the conservative newspapers exhibit their own tendency to report in line with their adopted ideology. This confirms that we can count on opinion mining technique based on topics in order to analyze opinion in a reliable fashion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6411-6418
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• 2015

To respond the climate change and environmental pollution, the studies on renewable energy policies are increasing. The renewable energy is a new growth engine technology represented by the green industry and green technology. At present, the investments for the renewable energy supply and technology development projects of three main strategy sectors such as sunlight, wind power and hydrogen fuel cell are implemented in our country, while they are still in the early stage, accordingly reducing those uncertainty for the research direction and investment fields is the most urgent issue among others. Thus, this study applied text mining method and multinominal topic model among the big data analysis methods on our country's newspaper articles concerning the renewable energy over the last 10 years, and then analyzed the core issues and global research trend, forecasting the renewable energy fields with the growth potential. It is predicted that these results of the study based on information and communication technology will be actively applied on the renewable energy fields.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.86-98
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• 2022

This study explored the agenda of conservative and liberal media in reporting COVID-19, and observed the effects of each media's partisan agenda-setting on the public with the same political orientation. To this end, researchers collected 5,286 articles on COVID-19 from five newspapers, and analyzed the survey data of 1,067 respondents. Next, the researchers extracted main agenda using LDA topic modeling and analyzed the correlation between newspapers' agenda and survey respondents' agenda. As results, 15 topics such as infection, vaccine, and economic crisis appeared as the media agenda, and the difference in major agenda between conservative and liberal media was found. On the other hand, the conservative media exerted an agenda-setting influence not only on the conservatives but also on the liberals, but the liberal media did not have a significant influence on the liberals. This study contributes to the methodological expansion of agenda-setting research by introducing a new way to confirm the effectiveness of agenda-setting by combining topic modeling and survey.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.