• Journal of Korean Library and Information Science Society
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• v.48 no.4
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• pp.235-258
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• 2017

The purpose of this study is to analyze research trends of archives management in Korea by comparing LDA (Latent Semantic Allocation) topic modeling, which is the most famous method in text mining, and HDP (Hierarchical Dirichlet Process) topic modeling, which is developed LDA topic modeling. Firstly we collected 1,027 articles related to archives management from 1997 to 2016 in two journals related with archives management and four journals related with library and information science in Korea and performed several preprocessing steps. And then we conducted LDA and HDP topic modelings. For a more in-depth comparison analysis, we utilized LDAvis as a topic modeling visualization tool. At the results, LDA topic modeling was influenced by frequently keywords in all topics, whereas, HDP topic modeling showed specific keywords to easily identify the characteristics of each topic.

• Information and Communications Magazine
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• v.34 no.6
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• pp.43-50
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• 2017

본고에서는 소셜 빅데이터에서 공공안전에 위협되고 사회적으로 이슈가 되는 재난사건을 추출하기 위한 방법으로 소셜 네트워크상에서 사용자 행동 분석과 시간분석을 반영한 토픽 모델링 기법을 알아본다. 소셜 사용자의 글 수, 리트윗 반응, 활동주기, 팔로워 수, 팔로잉 수 등 사용자의 행동 분석을 통하여 활동적이고 신뢰성 있는 사용자를 분류함으로써 트윗에서 스팸성과 광고성을 제외하고 이슈에 대해 신뢰성 높은 사용자가 쓴 트윗을 중요하게 반영한다. 또한, 트위터 데이터에서 새로운 이슈가 발생한 것을 탐지하기 위해 시간별 핵심어휘 빈도의 분포 변화를 측정하고, 이슈 트윗에 대해 감성 표현 분석을 통해 핵심이슈에 대해 사건 어휘를 추출한다. 소셜 빅데이터의 특성상 같은 날짜에 여러 이슈에 대한 트윗이 많이 생성될 수 있기 때문에, 트윗들을 토픽별로 그룹핑하는 것이 필요하므로, 최근 많이 사용되고 있는 LDA 토픽모델링 기법에 시간 특성과 사용자 특성을 분석한 시간상에서의 중요한 사건 어휘를 반영하고, 해당이슈에 대한 신뢰성 있는 사용자가 쓴 트윗을 중요시 반영하도록 토픽모델링 기법을 개선한 소셜 사건 탐지 방법에 대해 알아본다.

• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.149-152
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• 2014

본 연구에서는 저자명 모호성 해소를 위해 토픽모델링 기법을 사용하여 저자명을 식별 하였다. 기존의 토픽모델링은 용어 자질만을 고려하였지만 본 연구에서는 제 3의 메타데이터 자질을 활용하여 ACT(Author-Conference Topic Model) 모델과 DMR(Dirichlet-multinomial Regression) 토픽모델링을 대상으로 저자명 식별 성능을 평가, 비교하였다. 또한 수작업으로 저자 식별 작업을 한 데이터셋을 기반으로 저자 당 논문 수와 토픽 수에 차이를 두고 연구를 진행하였다. 그 결과 저자명 식별에 있어 ACT 모델보다 DMR 토픽모델링의 성능이 더 우수한 것을 알 수 있었다.

• Annual Conference of KIPS
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• 2022.05a
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• pp.464-467
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• 2022

기업의 사회적 책임에 대한 요구가 높아짐에 따라 기업의 지속 가능 경영 보고서 발간은 증가 추세를 보이고 있다. 그러나 이전까지의 연구는 지속가능성 및 기업의 재무적, 비재무적 연관성에 초점이 맞춰져 있었으며, 전통적인 토픽 모델링 기법만을 제한적으로 사용한다는 한계를 보였다. 본 연구에서는 Transformer 기반의 맥락을 고려한 토픽 모델링 기법을 도입하여 다양한 이해관계자 측면에서 이용 가능한 25 개의 주제를 도출하였다. 또한 동적 토픽 모델링(Dynamic Topic Modeling)을 통해 주제의 변화를 시계열적으로 파악했다.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.105-106
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• 2019

본 연구는 토픽모델링 기법을 국가의 연구개발 프로젝트에 적용하여 클러스터링하고 네트워크 분석을 통해 개별 클러스터와 R&D프로젝트를 평가하는 것에 관한 것이다.

• Journal of Digital Convergence
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• v.18 no.5
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• pp.75-87
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• 2020

The present study determined new value that can be created through the convergence between artificial intelligence technology (AIT) and all industries by deriving and thoroughly analyzing major issues related to artificial intelligence (AI). This study analyzes domestic articles related to AI using topic modeling method based on LDA algorithm. Keywords were extracted from 3,889 articles of eleven metropolitan newspapers, eight business newspapers and major broadcasting companies; articles were selected by searching for the keyword "artificial intelligence". Keywords were extracted by optimizing the relevance parameter λ to improve the measure of pointwise mutual information (PMI), which shows the association among the keywords of each topic, and topic names were inferred from keywords based on valid evidence. The extracted topics widely showed changes occurring throughout society, economy, industries, culture, and the support policy and vision of the government.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.811-819
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• 2023

Understanding the local conditions is a crucial factor in enhancing the success potential of overseas construction projects. This can be achieved through the analysis of news articles of the target market using topic modeling techniques. In this study, the authors aimed to analyze news articles using two topic modeling methods, namely Latent Dirichlet Allocation (LDA) and BERTopic, in order to determine the optimal approach for market condition analysis. To evaluate the alignment between the generated topics and the actual themes of the news documents, the research collected 6,273 BBC news articles, created ground truth data for individual news article topics, and finally compared this ground truth with the results of the topic modeling. The F1 score for LDA was 0.011, while BERTopic achieved a score of 0.244. These results indicate that BERTopic more accurately reflected the actual topics of news articles, making it more effective for understanding the overseas construction market.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.175-182
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• 2019

We classify topics on fall incidents occurring in construction sites using topic modeling among machine learning techniques and analyze the causes of the accidents according to each topic. In order to apply topic modeling based on latent dirichlet allocation, text data was preprocessed and evaluated with Perplexity score to improve the reliability of the model. The most common falling accidents happened to the daily workers belonging to small construction site. Most of the causes were not operated properly due to lack of safety equipment, inadequacy of arrangement and wearing, and low performance of safety equipment. In order to prevent and reduce the falling accidents, it is important to educate the daily workers of small construction site, arrange the workplace, and check the wearing of personal safety equipment and device.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.129-152
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• 2021

This study proposed a plan to develop wearable devices suitable for female college students by analyzing female college students' perceptions and preferences for wearable devices and their needs for health care using topic modeling and network analysis techniques. To this end, 2,457 posts related to health care and wearable devices were collected from the community used by S Women's University students. After preprocessing the collected posts and comment data, LDA-based topic modeling was performed. Through topic modeling techniques, major issues of female college students related to health care and wearable devices are derived, and bi-gram analysis and network analysis are performed on posts containing related keywords to understand female college students' views on wearable devices.

• Journal of the Korean Society for information Management
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• v.32 no.1
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• pp.153-169
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• 2015

The combined approach of using ego-centric network analysis and dynamic citation network analysis for refining the result of LDA-based topic modeling was suggested and examined in this study. Tow datasets were constructed by collecting Web of Science bibliographic records of White LED and topic modeling was performed by setting a different number of topics on each dataset. The multi-assigned top keywords of each topic were re-assigned to one specific topic by applying an ego-centric network analysis algorithm. It was found that the topical cohesion of the result of topic modeling with the number of topic corresponding to the lowest value of perplexity to the dataset extracted by SPLC network analysis was the strongest with the best values of internal clustering evaluation indices. Furthermore, it demonstrates the possibility of developing the suggested approach as a method of multi-faceted research trend detection.