• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.57-70
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• 2021

This study aims to investigate the security demand about the traffic policing by analyzing civil complaints. Latent Dirichlet Allocation(LDA) was applied to extract key topics for 2,062 civil complaints data related to traffic policing from e-People. And additional analysis was made of reports of violations, which accounted for a high proportion. In this process, the consistency and convergence of keywords and representative documents were considered together. As a result of the analysis, complaints related to traffic police could be classified into 41 topics, including traffic safety facilities, passing through intersections(signals), provisional impoundment of vehicle plate, and personal mobility. It is necessary to strengthen crackdowns on violations at intersections and violations of motorcycles and take preemptive measures for the installation and operation of unmanned traffic control equipments, crosswalks, and traffic lights. In addition, it is necessary to publicize the recently amended laws a implemented policies, e-fine, procedure after crackdown.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.81-92
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• 2022

In this study, interview scripts written in the dropout application was analyzed using BERTopic,, and parental influence, academic adaptation, university dissatisfaction was derived as major topics. An empirical study was conducted through a survey of 199 current students with researchmodel composed of those factors affecting dropout intention. The result shows that parental influence had a negative effect on academic adaptation and university satisfaction. Academic adaptation and university satisfaction had a negative effect on the dropout intention. parental influence did not directly affect the dropout intention, but had an indirect positive effect through academic adaptation and university/major satisfaction. The result shows that university satisfaction and academic adaptation is important factor to lower the dropout intention of students who chose current university by parental influence.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.367-375
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• 2021

This study aimed to identify the knowledge structure of researches on 'untact' and derived implications for directions for the studies using text mining. The study included network analysis and topic modelling of keywords and abstracts from 171 thesis published until October 2020. Centrality analysis showed that 'untact' studies had been focused on service, usage, consumption, technology and online. From the topic modelling, 6 topics such as 'COVID-19 and socio-technological change', 'needs and utilization of education contents', 'technology and service for user convenience', 'product marketing and sales', 'service design of the company', 'influence factors of usage and consumption' were extracted. Keywords that connect each topic were technology, service, usage, consumption, needs and factor. Exploratory analysis of 'untact' researches using text mining provides useful results for development of 'untact' studies.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.333-353
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• 2022

This study analyzed the relevance of the curriculum by applying topic modeling to book reports written as content area reading activities in the 'physics' class. In order to carry out the research, 332 physics book reports were collected to analyze the relevance among keywords and topics were extracted using STM. The result of the analysis showed that the main keywords of the physics book reports were 'thought', 'content', 'explain', 'theory', 'person', 'understanding'. To examine the influence and connection relationship of the derived keywords, the study presented degree centrality, between centrality, and eigenvetor centrality. As a result of the topic modeling analysis, eleven topics related to the physics curriculum were extracted, and the curriculum linkage could be drawn in three subjects (Physics I, Physics II, Science History), and six areas (force and motion, modern physics, wave, heat and energy, Western science history, and What is science). The analyzed results can be used as evidence for a more systematic implementation of content area reading activities which reflect the subject characteristics in the future.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.451-457
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• 2023

This study aims to identify the domestic research trends on nursing leadership and provide basic data that can be used for nursing leadership-related research and intervention development in Korea. To extract topics related to nursing leadership from 335 papers published in domestic academic journals from January 2012 to December 2021, the topic modeling technique was used. Keywords were extracted from abstracts, and literature searches were conducted in five domestic databases including DBpia, KISS, RISS, KM base, and Nanet. The study found that academic papers on nursing leadership have been steadily increasing, with self-leadership, self-efficacy, and education being identified as major topics. In addition, since self-leadership was the most frequently appearing keyword among the types of leadership, the study concluded that research on various forms of leadership should be more actively conducted. These research results are expected to contribute to enhancing understanding of nursing leadership in Korea. This study provides a new perspective on understanding the research trends on nursing leadership in Korea and analyzed the knowledge structure of domestic nursing leadership research, which is meaningful.

• Journal of Energy Engineering
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• v.29 no.2
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• pp.23-29
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• 2020

In this study, the last 10 years of news data on fine dust was collected and 80 topics are selected through LDA analysis. As a result, weather-related information made up the main words for the topic, and we can see that fine dust becomes a big issue below 10 degrees Celsius. The frequency of exposure to the media and the maximum concentration of fine dust are correlated with positive. Topics related to fine dust reduction measures and the government's comprehensive measures over the past decade, topics related to products such as air purifiers related to fine dust, topics related to policies protecting vulnerable people from fine dust, and topics on fine dust reduction through R&D were found to be major topics. Measures against fine dust as a social issue can be seen to be closely related to the government's policy.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.397-412
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• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.415-423
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• 2017

This study explored research trends by investigating articles published in the Journal of Korean Society of Fashion Design from 2001 through 2015. English key words and abstracts were analyzed using text mining and topic modeling techniques. The findings are as followings. By the text mining technique, 183 core terms, appeared more than 30 times, were derived from 7137 words used in total 338 articles' key words and abstracts. 'Fashion' and 'design' showed the highest frequency rate. After that, the well-received topic modeling technique, LDA, was applied to the collected data sets. Several distinct sub-research domains strongly tied with the previous fashion design field, except for topics such as fashion brand marketing and digital technology, were extracted. It was observed that there are the growing and declining trends in the research topics. Based on findings, implication, limitation, and future research questions were presented.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.4
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• pp.19-28
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• 2017

Companies and Governments have Mainly used the Delphi Technique to Understand Research or Technology Trends. Because this Technique has the Disadvantage of Consuming a Large Amount of Time and Money, this Study Attempted to Understand and Predict Science and Technology Trends using the Topic Modeling Technique Latent Dirichlet Allocation (LDA). To this end, 20 Specific Artificial Intelligence (AI) Technologies were Extracted From the Abstracts of the US Patent Documents on AI. With Regard to the Extracted Specific Technologies, Core Technologies were Identified, and then these were Divided into Hot and Cold Technologies though a Trend Analysis on their Annual Proportions. Text/Word Searching, Computer Management, Programming Syntax, Network Administration, Multimedia, and Wireless Network Technology were Derived From Hot Technologies. These Technologies are Key Technologies that are Actively Studied in the Field of AI in Recent Years. The Methodology Suggested in this Study may be used to Analyze Trends, Derive Policies, or Predict Technical Demands in Various Fields such as Social Issues, Regional Innovation, and Management.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.35-45
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• 2021

The purpose of this study is to understand the characteristics and trends of active senior. As the baby boom generation become the age of the elderly, they are more active than senior. These seniors are called active seniors, a new consumer group. Many countries and companies are also interested in providing relevant policies and services, but there is lack of researches on active senior trends. This study collects the 8,740 posts related to active seniors on social media from January 1st, 2018 to June 31st, 2021, and conducted keyword frequency analysis, TF-IDF analysis and LDA topic modeling. Through LDA topic modeling, topics are classified into 10 categories: lifestyle, benefits, shopping, government business, government education, health, society and economy, care industry, silver housing, leisure. The results of this study can be utilized as fundamental data to help understand the academic and industrial aspects of active senior.