• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.175-198
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• 2023

This study aimed to investigate the difference in response to reading as shown in book reports by personality type and interest type. For this purpose, personality type analysis data, interest type analysis data, and book report data written in subject reading activities were collected from 81 third graders at D Science High School in Daejeon. Topic analysis was conducted on the collected book reports, and the probability of a topic being mentioned was statistically tested according to personality type (thinking type, feeling type) and interest type (investigative type, types other than investigative). Subsequently, the conceptual connection structure of words was measured by keyword network analysis, and the analysis results of topic modeling were complemented by the centrality index. As a result of the study, the topic regression analysis showed statistically significant differences between thinking type (T) and feeling type (F) in topic 2 (understanding and studying) and topic 3 (reading and thinking), and statistically significant differences between investigative type and non-investigative type in topic 2 (understanding and studying). The results of this study can be used as a basis for tailored book recommendations and personalized reading education.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.58-71
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• 2019

The purpose of this study is to explore the multi-dimensionality and the differences of the career success that is revealed by the employee's perception. In order to fulfill the research purpose, LDA topic modeling has applied to extract latent topics of career success from 126 Korean employees' open-end survey questionnaires. The extracted latent topics are social recognition, continuing service within an organization, expertise, financial rewards, and pursuing personal meaning. The occurrence probability of each topic was different by individual characteristics such as gender, education, position. Study findings showed there is multi-dimensionality in career success, and there are differences of topic occurrence probability by demographic characteristics. Additionally, this study showed how to apply the recently developed machine learning approach in order to reduce the researcher's bias by adapting the LDA topic modeling to the qualitative open-ended survey data.

• Journal of Industrial Convergence
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• v.18 no.1
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• pp.73-78
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• 2020

To analyze the main topics related to the use of blockchain technology, the Topic Modeling Technique was applied to the 'Blockchain Technology Utilization' big data shown in newspaper articles. To this end, from 2013 to 2019, when newspaper articles on the use of blockchain technology first appeared, the topics were extracted from 21 newspapers and analyzed by time to 15,537 articles. As a result of the analysis, articles related to the utilization of blockchain technology have increased exponentially since 2015 and focused on IT_science and economics. Key words related to cryptocurrency, bitcoin and virtual currency were weighted high, although they differed depending on time. Blockchain technology, which had focused on financial transactions, gradually expanded to big data, Internet of Things and artificial intelligence. As a result, changes in corporate topics were also made together to expand into various fields at banks for financial transactions, focusing on large and global companies. The study showed how these topics were changing, along with the main topics in newspaper articles related to the use of blockchain technology.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.73-80
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• 2022

In this study, prior studies of 280 small business owners in Korea over the past two decades were comprehensively analyzed through keyword network and LDA topic modeling analysis, and overall views and trends in academia were examined. As core keywords, "sales" and "protection," which conflict with each other but are essential for stable and sustainable growth were selected, and 7 topics (Topic 1: start-up, topic 2: digital, topic 3: tax system, topic 4: capability, topic 5: coexistence, topic 6: regulation, and topic 7: funding) were drawn up. Based on the results of the analysis, the need to improve digital maturity for the continued growth and development of small business owners was raised, and the response at the pan-ministerial level and the stability of the performance of functions that can survive even after the new administration to solve the economic damage problems facing small business owners were suggested. In addition, attention to the long-term, speed, detail, and direction of government support in a new way, and a flexible approach to the negative way in which pre-allowance and post-regulation is given were suggested.

• 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.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.228-230
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• 2015

방송 대본은 방송 콘텐츠에 대해 얻을 수 있는 가장 주요한 텍스트 데이터 중에 하나이다. 본 논문에서는 토픽 모델을 통해 방송 대본 분석을 수행하고 그 결과를 제시한다. 방송 대본을 토픽 모델로 학습하기 위해 대본의 장면 단위로 문서를 구성하여 학습하여 대본의 장면을 분석하고 등장인물 단위로 문서를 구성하여 등장인물을 분석하여 그 특징을 살펴본다. 토픽 모델을 사용하여 방송 대본을 분석하는 과정에서 방송 대본이 가지는 특징을 분석하고 그로부터 향후 연구방향에 대해 논의한다.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.17-27
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• 2020

News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.

• 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 Korea Information Processing Society Conference
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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.