• Journal of Information Processing Systems
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• v.14 no.1
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• pp.79-100
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• 2018

Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summarization techniques by surveying recent evaluation methodologies. At the end of the survey, we emphasize on both current and future research challenges in this domain through a level of depth analysis of the most recent summarization approaches.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.275-277
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• 2019

The purpose of this study is to classify the topics related to the suicide papers published so far and to identify the proporations of the main topics and the trends of the topics over the past 20 years. For this purpose, a text mining technique used in big data analysis was used as a data base of the Korean Journal of Citation Index (KCI), where information sharing about the papers is most active. This study, which grasps the trends of suicide related research according to the changes of the times, will become a basic data for establishing a strategy to adapt the academic direction related to suicide in the future.

• 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 Information Technology Services
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• v.22 no.2
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• pp.93-127
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• 2023

This study analyzed the CEO New Year's addresses of major Korean companies, extracting key topics for employees via text mining techniques. An intended contribution of this study is to assist reporters, analysts, and researchers in gaining a better understanding of the New Year's addresses by elucidating the implicit and implicative features of messages within. To this end, this study collected and analyzed 545 New Year's addresses published between 2012 and 2021 by the top 66 Korean companies in terms of market capitalization. Research methodologies applied include text clustering, word embedding of keywords, frequency analysis, and topic modeling. Our main findings suggest that the messages in the New Year's addresses were categorized into nine topics-organizational culture, global advancement, substantial management, business reorganization, capacity building, market leadership, management innovation, sustainable management, and technology development. Next, this study further analyzed the managerial significance of each topic and discussed their characteristics from the perspectives of time, industry, and corporate groups. Companies were typically found to emphasize sound management, market leadership, and business reorganization during economic downturns while stressing capacity building and organizational culture during market transition periods. Also, companies belonging to corporate groups tended to emphasize founding philosophy and corporate culture.

• Korea Trade Review
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• v.47 no.4
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• pp.137-159
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• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.93-104
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• 2024

This study conducts an in-depth analysis of user reviews from three leading metaverse platforms - Minecraft, Roblox, and Zepeto - using advanced topic modeling techniques to uncover key factors for business success. By examining a substantial dataset of user feedback, we identified and categorized the main themes and concerns expressed by users. Our analysis revealed that common issues across all platforms include technical functionality problems, user engagement and interest, payment concerns, and connection difficulties. Specifically, Minecraft users highlighted the importance of adventure and creativity, Roblox users expressed significant concerns about security and fraud, and Zepeto users focused heavily on the fairness of the in-game economy. The findings suggest that for metaverse platforms to achieve sustained success, they must prioritize the resolution of technical issues, enhance features that foster user engagement, ensure reliable connectivity, and address platform-specific concerns such as security for Roblox and payment fairness for Zepeto. These insights provide valuable guidance for developers and business strategists, emphasizing the need for robust technical infrastructure, engaging and diverse content, seamless user access, and transparent and fair economic systems. By addressing these key areas, metaverse platforms can improve user satisfaction, build a loyal user base, and secure long-term success in an increasingly competitive market.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.53-79
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• 2017

The main purpose of this study was to detect and classify public perceptions toward smog disasters on Twitter using topic modeling. To help achieve these objectives and to identify gaps in the literature, this research carried out a literature review on public opinions toward smog disasters and topic modeling. The literature review indicated that there are huge gaps in the related literature. In this research, this author formed five research questions to fill the gaps in the literature. And then this study performed research steps such as data extraction, word cloud analysis on the cleaned data, building the network of terms, correlation analysis, hierarchical cluster analysis, topic modeling with the LDA, and stream graphs to answer those research questions. The results of this research revealed that there exist huge differences in the most frequent terms, the shapes of terms network, types of correlation, and smog-related topics changing patterns between New York and London. Therefore, this author could find positive answers to the four of the five research questions and a partially positive answer to Research question 4. Finally, on the basis of the results, this author suggested policy implications and recommendations for future study.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• The Journal of Engineering Geology
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• v.34 no.3
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• pp.429-445
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• 2024

The Journal of Engineering Geology (JEG), a leading academic journal in the field of engineering geology in South Korea, was first published in 1991 and has since been publishing academic papers and research findings. While several literature reviews have been undertaken on specific research areas in recent decades, comprehensive reviews focusing on JEG have been relatively limited. To address this gap, this study applied the latent Dirichlet allocation (LDA) model to analyze the main research topics and trends, and undertook network analysis to identify relationships between topics over different periods. Results for the LDA indicate seven key research topics categorized into three trends: Classic, Emerging and Stable topics. Classic topics include 'Geophysics' and 'Structural geology', which were major subjects in the early years, with their focus shifting to other areas over time. Emerging topics such as 'Hydrogeology' and 'Geohazards' have gained prominence in recent years. Stable topics including 'Geotechnical structures', 'Geomechanics', and 'Environmental geology' have maintained consistent research interest. Network analysis revealed that Structural geology was the central topic prior to 2008, while Geotechnical structures became the focal point of research after 2008, with a shift in research focus. The results of this study contribute to our understanding of research trends and the development of JEG, providing insights for the setting of future research directions.

• Journal of Engineering Education Research
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• v.19 no.1
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• pp.37-46
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• 2016

The purposes of the study were to examine engineering students' concerns and problems while they were choosing a project topic in interdisciplinary collaboration and to suggest ways to support them in an early stage of collaboration phase. To answer the research questions, we conducted a case study with engineering participants in GCTI 2015, an interdisciplinary collaborative and creative group project. Multiple data sources including focus group interviews, online survey and researchers' observation notes were used to triangulate research findings. We found four main concerns of engineering students. These concerns include (1) lack of self-efficacy, (2) limited resources, (3) lack of shared, meaningful, and common goals, and (4) lack of content knowledge. Based on these concerns we proposed four supports in an early stage of the collaborative project. These supports includes (1) implementing an orientation program, (2) providing opportunities for social interactions, (3) providing expert feedback, and (4) providing consultation for team building.