• Communications of Mathematical Education
• /
• v.37 no.4
• /
• pp.615-633
• /
• 2023

This study analyzed Korean studies up to August 2023 to suggest the direction of future research on basic academic abilities in mathematics. For this purpose, frequency analysis and LDA-based topic modeling were conducted on the Korean abstracts of 197 domestic studies. The results showed that, first, 'academic achievement', 'impact', 'effect', and 'factors' were all ranked at the top of the TFs and TF-IDFs. Second, as a result of LDA-based topic modeling, five topics were identified: causes of basic academic abilities deficiency, learning status of math underachievers, teacher expertise in teaching math underachievers, supporting programs for math underachievers, and results of National Assessment of Educational Achievement. As a direction for future research, this study suggests focusing on the growth of math underachievers, systematizing the programs provided to students who need learning support in mathematics, and developing teacher expertise in teaching math underachievers.

• Journal of Korean Library and Information Science Society
• /
• v.47 no.4
• /
• pp.1-18
• /
• 2016

The purpose of this study is to explore application of topic modeling for topic analysis of book report. Topic modeling can be understood as one method of topic analysis. This analysis was conducted with texts in 23 book reports using LDA function of the "topicmodels" package provided by R. According to the result of topic modeling, 16 topics were extracted. The topic network was constructed by the relation between the topics and keywords, and the book report network was constructed by the relation between book report cases and topics. Next, Centrality analysis was conducted targeting the topic network and book report network. The result of this study is following these. First, 16 topics are shown as network which has one component. In other words, 16 topics are interrelated. Second, book report was divided into 2 groups, book reports with high centrality and book reports with low centrality. The former group has similarities with others, the latter group has differences with others in aspect of the topics of book reports. The result of topic modeling is useful to identify book reports' topics combining with network analysis.

• Journal of Korea Society of Industrial Information Systems
• /
• v.25 no.3
• /
• pp.39-51
• /
• 2020

This study will examine the social issues and consumer awareness of sharing parking through the method text mining. In this experiment, the topic by keyword was extracted and analyzed using TFIDF (Term frequency inverse document frequency) and LDA (Latent dirichlet allocation) technique. As a result of categorization by topic, citizens' complaints such as local government agreements, parking space negotiations, parking culture improvement, citizen participation, etc., played an important role in implementing shared parking services. The contribution of this study highly differentiated from previous studies that conducted exploratory studies using corporate and regional cases, and can be said to have a high academic contribution. In addition, based on the results obtained by utilizing the LDA analysis in this study, there is a practical contribution that it can be applied or utilized in establishing a sharing economy policy for revitalizing the local economy.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.99-107
• /
• 2024

In an environment where computer-related technologies are rapidly changing, cyber threats continue to emerge as they are advanced and diversified along with new technologies. Therefore, in this study, we would like to collect security-related news articles, conduct LDA topic modeling, and examine trends. To that end, news articles from January 2020 to August 2023 were collected and major topics were derived through LDA analysis. After that, the flow by topic was grasped and the main origin was analyzed. The analysis results show that ransomware attacks in 2021 and hacking of virtual asset exchanges in 2023 are major issues in the recent security sector. This allows you to check trends in security issues and see what research should be focused on in the future. It is also expected to be able to recognize the latest threats and support appropriate response strategies, contributing to the development of effective security measures.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.1
• /
• pp.31-48
• /
• 2024

The domestic automotive industry must consider a strategic shift from traditional automotive component manufacturing to align with future trends such as connectivity, autonomous driving, sharing, and electrification. This research conducted topic modeling on R&D projects in the future automotive sector funded by the Ministry of Trade, Industry, and Energy from 2013 to 2021. We found that topics such as sensors, communication, driver assistance technology, and battery and power technology remained consistently prominent throughout the entire period. Conversely, topics like high-strength lightweight chassis were observed only in the first period, while topics like AI, big data, and hydrogen fuel cells gained increasing importance in the second and third periods. Furthermore, this research analyzed the areas of concentrated investment for each period based on topic-specific government investment amounts and investment growth rates.

• Journal of Convergence for Information Technology
• /
• v.7 no.6
• /
• pp.53-59
• /
• 2017

This study was attempted to identify the domestic research related to depression and stress. The subjects of the analysis were 1,875 college degree theses thrown in the National Assembly Library searched by the depression and stress keyword as of November 30, 2016. The analysis method visualizes atypical data with Word Cloud, which is one of the text mining techniques. We also used the R'LDA package and LDA to classify treatment and subjects. As a result of the analysis, 233(12.4%) of the total papers with therapeutic keywords were found. Application of treatment methods was art therapy, music therapy, horticultural therapy, cognitive behavior therapy, clinical art therapy, cognitive therapy, psychological therapy, depression treatment, group therapy, laughter treatment sequence. The study subjects were adolescents, elderly, patient, mother, child, female, parents, and college students in order. The results of LDA topic analysis for adolescents were classified into four topics: self-support, treatment program, relationship effect, and variable study.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.5
• /
• pp.207-214
• /
• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.2
• /
• pp.308-313
• /
• 2021

In this study, tweets about the 2019 Koreas-US DMZ Summit were collected along with a time sequence and analyzed by a sequential topic modeling method, Dynamic Topic Modeling(DTM). In microblogging services such as Twitter, unstructured data that mixes news and an opinion about a single event occurs at the same time on a large scale, and information and reactions are produced in the same message format. Therefore, to grasp a topic trend, the contextual meaning can be found only by performing pattern analysis reflecting the characteristics of sequential data. As a result of calculating the DTM after obtaining the topic coherence score and evaluating the Latent Dirichlet Allocation(LDA), 30 topics related to news reports and opinions were derived, and the probability of occurrence of each topic and keywords were dynamically evolving. In conclusion, the study found that DTM is a suitable model for analyzing the trend of integrated topics in a specific event over time.

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

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