• 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.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.99-132
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• 2022

The purpose of this study is to propose AET (Augmented and Extended Topics), a novel method of synthesizing both LDA and BERTopic results, and to analyze the recently published LIS articles as an experimental approach. To achieve the purpose of this study, 55,442 abstracts from 85 LIS journals within the WoS database, which spans from January 2001 to October 2021, were analyzed. AET first constructs a WORD2VEC-based cosine similarity matrix between LDA and BERTopic results, extracts AT (Augmented Topics) by repeating the matrix reordering and segmentation procedures as long as their semantic relations are still valid, and finally determines ET (Extended Topics) by removing any LDA related residual subtopics from the matrix and ordering the rest of them by F₁ (BERTopic topic size rank, Inverse cosine similarity rank). AET, by comparing with the baseline LDA result, shows that AT has effectively concretized the original LDA topic model and ET has discovered new meaningful topics that LDA didn't. When it comes to the qualitative performance evaluation, AT performs better than LDA while ET shows similar performances except in a few cases.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.5-30
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• 2024

The purpose of this study is to extract topics from experimental data using the topic modeling methods(LDA, Top2Vec, and BERTopic) and compare the characteristics and differences between these models. The experimental data consist of 55,442 papers published in 85 academic journals in the field of library and information science, which are indexed in the Web of Science(WoS). The experimental process was as follows: The first topic modeling results were obtained using the default parameters for each model, and the second topic modeling results were obtained by setting the same optimal number of topics for each model. In the first stage of topic modeling, LDA, Top2Vec, and BERTopic models generated significantly different numbers of topics(100, 350, and 550, respectively). Top2Vec and BERTopic models seemed to divide the topics approximately three to five times more finely than the LDA model. There were substantial differences among the models in terms of the average and standard deviation of documents per topic. The LDA model assigned many documents to a relatively small number of topics, while the BERTopic model showed the opposite trend. In the second stage of topic modeling, generating the same 25 topics for all models, the Top2Vec model tended to assign more documents on average per topic and showed small deviations between topics, resulting in even distribution of the 25 topics. When comparing the creation of similar topics between models, LDA and Top2Vec models generated 18 similar topics(72%) out of 25. This high percentage suggests that the Top2Vec model is more similar to the LDA model. For a more comprehensive comparison analysis, expert evaluation is necessary to determine whether the documents assigned to each topic in the topic modeling results are thematically accurate.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.66-67
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• 2023

최근 들어, 현대사회는 예측이 불가능한 다양한 위험성들이 존재하여 글로벌 의존도가 높은 항만물류산업의 위험부담이 증가하고 있다. 이에 본 연구에서는 항만산업의 안전성에 영향을 미치는 요인을 알아보기 위해 과거부터 현재까지 국내 항만 안전성에 영향을 미친 이슈들을 시계열적으로 살펴보고자 하였다. 이를 위하여 국내를 대표하는 부산항의 항만 안전성과 관련된 뉴스 기사 텍스트 데이터를 활용하여 LDA 토픽모델링 분석을 진행하여 부산항 항만안전 주요 이슈들의 동향을 살펴보고자 하였다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.402-403
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• 2023

본 연구의 목적은 LDA 토픽 모델링을 활용하여 한국어 SNS데이터에 분석을 통해 우리나라의 여가활동, 일과 직업, 주거와 생활의 동향을 살펴보는 것이다. AI Hub에서 제공하는 한국어 SNS데이터를 수집하고 형태소 분석, 전처리 과정을 거친 후 coherence score을 토대로 최적의 토픽 수를 결정하여 토픽을 추출하였다. 도출한 트렌드를 바탕으로 경영, 마케팅 분야에 미치는 영향을 예측할 수 있을 것으로 기대한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.423-432
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• 2022

This paper analyzes a total of 10,599 papers related to COVID-19 from January 2020 to July 2022 collected from the KCI site using LDA topic modeling so that academic researchers can understand the overall research trend. The results of LDA topic modeling are analyzed by major research categories so that academic researchers can easily figure out topics in their research fields. Then, the detailed research category information in which a lot of research is done by topic is analyzed. It is very important for academic researchers to understand the trend of research topics over time. Therefore, in this paper, the trend of topics is analyzed and presented using time series decomposition.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.9-18
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• 2020

The research objectives investigates main research topics and trends in the information and communication technology(ICT) field, Korea using LDA(Latent Dirichlet Allocation), one of the topic modeling techniques. The experimental dataset of ICT research and development(R&D) project of 5,200 was acquired through matching with the EZone system of IITP after downloading R&D project dataset from NTIS(National Science and Technology Information Service) during recent five years. Consequently, our finding was that the majority research topics were found as intelligent information technologies such as AI, big data, and IoT, and the main research trends was hyper realistic media. Finally, it is expected that the research results of topic modeling on the national R&D foundation dataset become the powerful information about establishment of planning and strategy of future's research and development in the ICT field.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.27-33
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• 2024

The environment of academic research is continuously changing due to the increase of information, which raises the need for an effective way to analyze and organize large amounts of documents. In this paper, we propose Performance Improvement of Topic Modeling using BART(Bidirectional and Auto-Regressive Transformers) based Document Summarization. The proposed method uses BART-based document summary model to extract the core content and improve topic modeling performance using LDA(Latent Dirichlet Allocation) algorithm. We suggest an approach to improve the performance and efficiency of LDA topic modeling through document summarization and validate it through experiments. The experimental results show that the BART-based model for summarizing article data captures the important information of the original articles with F1-Scores of 0.5819, 0.4384, and 0.5038 in Rouge-1, Rouge-2, and Rouge-L performance evaluations, respectively. In addition, topic modeling using summarized documents performs about 8.08% better than topic modeling using full text in the performance comparison using the Perplexity metric. This contributes to the reduction of data throughput and improvement of efficiency in the topic modeling process.

• 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 Korean Association For Science Education
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• v.42 no.2
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• pp.185-200
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• 2022

The purpose of this study is to understand the trends and changes of the articles publishing the Journal of the Korean Association for Science Education(JKASE) in the past forty-four years. To this end, Latent Dirichlet Allocation(LDA) topic modeling analysis was performed on a total of 2,115 English abstracts of papers published in the JKASE from 1978 to 2021. As a result of LDA topic modeling analysis, a total of 23 topics were extracted, and each topic was presented with its related keywords and articles. Next, in order to examine how these topics have changed over time, we visualized the average weights of each topic for a 4-year cycle by using heatmaps. The topics that have risen or fallen were identified. The results of this study provide new insights into science education research in Korea in terms of revealing not only traditional research topics that have been consistently studied but also the topics that have changed in response to the development of educational philosophy or research methods, social or policy demands related to science education.