• Educational Technology International
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• v.25 no.1
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• pp.1-25
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• 2024

This study aims to analyze the impact of learners' usability perceptions of topic modeling-based visual feedback and generative AI interpretation on reflection levels in online discussions. To achieve this, we asked 17 students in the Department of Korean language education to conduct an online discussion. Text data generated from online discussions were analyzed using LDA topic modeling to extract five clusters of related words, or topics. These topics were then visualized in a network format, and interpretive feedback was constructed through generative AI. The feedback was presented on a website and rated highly for usability, with learners valuing its information usefulness. Furthermore, an analysis using the non-parametric Mann-Whitney U test based on levels of usability perception revealed that the group with higher perceived usability demonstrated higher levels of reflection. This suggests that well-designed and user-friendly visual feedback can significantly promote deeper reflection and engagement in online discussions. The integration of topic modeling and generative AI can enhance visual feedback in online discussions, reinforcing the efficacy of such feedback in learning. The research highlights the educational significance of these design strategies and clears a path for innovation.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.115-128
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• 2022

The purpose of this study is to conduct keyword trend analysis using articles data on the impact of Covid-19 in the aviation in dustry. In this study, related articles were extracted centering on the keyword "Airline" by dividing the period of 6months before and after Covid-19 occurrence. After that, Topic modeling(LDA) was performed. Through this, The main topic was extracted in the event of an epidemic such as Covid-19, It is expected to be used as primary data to predict the aviation industry's impact when occurrence like Covid-19.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.267-291
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• 2021

Due to the global pandemic caused by COVID-19 in 2020, there have been major changes in the education sites. Universities have fully introduced remote learning, which was considered as an auxiliary education, and non-face-to-face classes have become commonplace, and professors and students are making great efforts to adapt to the new educational environment. In order to improve the quality of non-face-to-face lectures amid these changes, it is necessary to study the factors affecting lecture satisfaction. Therefore, This paper presents a new methodology using big data to identify the factors affecting university lecture satisfaction changed before and after COVID-19. We use Topic Modeling method to analyze lecture reviews before and after COVID-19, and identify factors affecting lecture satisfaction. Through this, we suggest the direction for university education to move forward. In addition, we can identify the factors of satisfaction and dissatisfaction of lectures from multiangle by establishing a topic classification model with an F1-score of 0.84 based on KoBERT, a deep learning language model, and further contribute to continuous qualitative improvement of lecture satisfaction.

• Informatization Policy
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• v.30 no.1
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• pp.41-66
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• 2023

This study aims to reveal the direction of municipal ordinances for smart cities, while focusing on 74 municipal ordinances from 72 municipal governments through topic modeling. As a result, the main keywords that show a high frequency belong to establishment and operations of the Smart City Committee. From the result of topic modeling Latent Dirichlet Allocation(LDA), it classifies municipal ordinances for smart cities into eight topics as follows: Topic 1(security for process of smart cities), Topic 2(promotion of smart city industry), Topic 3(composition of a smart city consultative body for local residents), Topic 4(support system for smart cities), Topic 5(management for personal information), Topic 6(use of smart city data), Topic 7(implementation for intelligent public administration), and Topic 8(smart city promotion). As for topic categorization by region, Topics 5, 6, and 8 which are mostly related to the practical operation of smart cities have a significant portion of municipal ordinances for smart cities in the Seoul metropolitan area. Then, Topics 2, 3, and 4 which are mostly related to the initial implementation of smart cities have a significant portion of municipal ordinances for smart cities in provincial areas.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.169-184
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• 2016

Smartphone use is rapidly spreading due to the advantage of being able to connect to the Internet anytime, anywhere--and mobile app development is developing accordingly. The characteristic of the mobile app market is the ability to launch one's app into foreign markets with ease as long as the platform is the same. However, a large amount of prior research asserts that consumers behave differently depending on their culture and, from this perspective, various studies comparing the differences between consumer behaviors in different countries exist. Accordingly, this research, which uses online product reviews (OPRs) in order to analyze the cultural differences in consumer behavior comparatively by nationality, proposes to compare the U.S. and South Korea by selecting ten apps which were released in both countries in order to perform a sentimental analysis on the basis of star ratings and, based on those ratings, to interpret the sentiments in reviews. This research was carried out to determine whether, on the basis of ratings analysis, analysis of review contents for sentiment differences, analysis of LDA topic modeling, and co-occurrence analysis, actual differences in online reviews in South Korea and the U.S. exist due to cultural differences. The results confirm that the sentiments of reviews for both countries appear to be more negative than those of star ratings. Furthermore, while no great differences in high-raking review topics between the U.S. and South Korea were revealed through topic modeling and co-occurrence analyses, numerous differences in sentiment appeared-confirming that Koreans evaluated the mobile apps' specialized functions, while Americans evaluated the mobile apps in their entirety. This research reveals that differences in sentiments regarding mobile app reviews due to cultural differences between Koreans and Americans can be seen through sentiment analysis and topic modeling, and, through co-occurrence analysis, that they were able to examine trends in review-writing for each country.

• 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 the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.247-264
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• 2021

This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.1-11
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• 2022

In this paper, numerical values are derived using topic modeling among data-based evaluation methodologies discussed by various research institutes. In addition, we will focus on the ICT field to see if there is a difference in policy perception between the national R&D project and standing committee. First, we create model for classifying ICT documents by learning R&D project data using HAN model. And we perform LDA topic modeling analysis on ICT documents classified by applying the model, compare the distribution with the topics derived from the R&D project data and proceedings of standing committees. Specifically, a total of 26 topics were derived. Also, R&D project data had professionally topics, and the standing committee-discuss relatively social and popular issues. As the difference in perception can be numerically confirmed, it can be used as a basic study on indicators that can be used for future policy or project evaluation.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.