• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.661-670
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• 2021

Research trends can be usefully used to determine the importance of research topics by period, identify insufficient research fields, and discover new fields. In this study, research trends of urban spaces, where various problems are occurring due to population concentration and urbanization, were analyzed by topic modeling. The analysis target was the abstracts of papers listed in the Korea Citation Index (KCI) published between 2002 and 2019. Topic modeling is an algorithm-based text mining technique that can discover a certain pattern in the entire content, and it is easy to cluster. In this study, the frequency of keywords, trends by year, topic derivation, cluster by topic, and trend by topic type were analyzed. Research in urban regeneration is increasing continuously, and it was analyzed as a field where detailed topics could be expanded in the future. Furthermore, urban regeneration is now becoming a regular research field. On the other hand, topics related to development/growth and energy/environment have entered a stagnation period. This study is meaningful because the correlation and trends between keywords were analyzed using topic modeling targeting all domestic urban studies.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.29-40
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• 2022

The aim of this study was to analyze research trends of social service quality from 2007 to 2020 based on text mining and topic modeling. Our focus was to provide foundational materials for social service improvement by discovering the latent meaning of relevant research papers. We collected 97 scholarly articles on social service, social welfare service, and quality from RISS, and implemented two segments of text mining analysis. Our results showed that the first section included 38 papers and the second 59, indicating 6.9 articles annually. Word frequency results demonstrated that the common keywords of both sections were 'service', 'quality', 'social service', 'satisfaction', 'users', 'quality control', 'reuse', 'policy', 'voucher', etc. TF-IDF suggested that 'social service', 'satisfaction', 'users', 'customer satisfaction', 'revisiting', 'voucher', 'quality', 'assisted living facility', 'quality control', 'community service investment business', etc., were represented in both categories. Lastly, topic modeling analysis revealed that the first segment displayed 'types of care services', 'service costs', 'reuse', 'users based', and 'job creation', whereas the second presented 'service quality', 'public value', 'management system of human resources', 'service provision system', and 'service satisfaction'. Future directions of social service quality were discussed based on the results.

• Journal of odor and indoor environment
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• v.17 no.4
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• pp.322-329
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• 2018

The objective of this study is to identify the research trend in the field of indoor environment in Korea. We collected 419 papers published in the Journal of the Korean Society for indoor environment between 2004 and 2018, and attempted to produce datasets using a topic modeling technique, Latent Dirichlet Allocation(LDA). The result of topic modeling showed that 8 topics ("VOCs investigation", "Subway environment", "Building thermal environment", "School health", "Building particulate matter", "Asbestos risk", "Radon risk", "Air cleaner and treatment") could be extracted using Gibbs sampling method. In terms of topic trends, investigation of volatile organic compounds, subway environment, school health, and building particulate matter showed a decreasing tendency, while the building thermal environment, asbestos risk, radon risk, air cleaners, and air treatment showed an increasing tendency. The results of this topic modeling could help us to understand current trends related indoor environment, and provide valuable information in developing future research and policy frameworks.

• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• Korea Trade Review
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• v.48 no.1
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• pp.55-81
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• 2023

The purpose of this study is to analyze the main issues and trends of Korean trade, and to draw implications for future research regarding trade rules. A total of 476 academic journal are analyzed using English keyword searched for 'Trade Rules' from 2000 to July 2022 in the Korean Journal Citation Index data base. The analysis methodology includes co-occurrence network and topic trend analysis which is a kind of text mining methods. The results shows that key words representing Korea's trade trend fall into four categories in which the number of research journals has rapidly increased, which are Topic 4 (Investment Treaty), Topic 7 (Trade Security), Topic 8 (China's Protectionism), and Topic 11 (Trade Settlement). The major background for these topics is the tension between the United States and China threatening the existing international trade system. A detailed study for China's protectionism, changes in trade security system, and new investment agreements, and changes in payment methods will be the challenges in near future.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.1-31
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• 2023

This study conducted user experience evaluation by introducing various text mining techniques along with topic modeling techniques for mobile menstrual cycle measurement applications that are closely related to women's health and analyzed the results by combining them with a honeycomb model. To evaluate the user experience revealed in the menstrual cycle measurement application review, 47,117 Korean reviews of the menstrual cycle measurement application were collected. Topic modeling analysis was conducted to confirm the overall discourse on the user experience revealed in the review, and text network analysis was conducted to confirm the specific experience of each topic. In addition, sentimental analysis was conducted to understand the emotional experience of users. Based on this, the development strategy of the menstrual cycle measurement application was presented in terms of accuracy, design, monitoring, data management, and user management. As a result of the study, it was confirmed that the accuracy and monitoring function of the menstrual cycle measurement of the application should be improved, and it was observed that various design attempts were required. In addition, the necessity of supplementing personal information and the user's biometric data management method was also confirmed. By exploring the user experience (UX) of the menstrual cycle measurement application in-depth, this study revealed various factors experienced by users and suggested practical improvements to provide a better experience. It is also significant in that it presents a methodology by combines topic modeling and text network analysis techniques so that researchers can closely grasp vast amounts of review data in the process of evaluating user experiences.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.33-49
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• 2018

The importance of convergence activities among business is increasing due to the necessity of designing and developing new products to satisfy various customers' needs. In particular, decision makers such as CEOs are required to participate in networks between entrepreneurs for being connected with valuable convergence partners. Moreover, it is important for entrepreneurs not only to make a large number of network connections, but also to understand the networking relationship with entrepreneurs with similar topic information. However, there is a difficult limit in collecting the topic information that can show the lack of current status of business and the technology and characteristics of entrepreneur in industry sector. In this paper, we solve these problems through the topic extraction method and analyze the business network in three aspects. Specifically, there are C, S, T-Layer models, and each model analyzes amount of entrepreneurs relationship, network centrality, and topic similarity. As a result of experiments using real data, entrepreneur need to activate network by connecting high centrality entrepreneur when the corporate relationship is low. In addition, we confirmed through experiments that there is a need to activate the topic-based network when topic similarity is low between entrepreneurs.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.139-149
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• 2020

New platform governments will play a role to pull intelligent information technology to drive new ecological government innovation and sustainable development in which the government and people work together. On this, in order to establish the platform of the platform government, we will look at recent research trends and lay the foundation for future policy directions and research bases. using Text Mining method, and went through Topic modeling for the collected text data and network analysis was conducted. According to the result, based on latent topic, the stronger the connection center, the weaker the relationship. Through this study, we hope that discussions will take place in a variety of ways to improve the understanding of the supply and demand approach of Korea's platform government and implement appropriate change management methods such as service public base and service provision in accordance with the value and potential topics of platform government.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.239-252
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• 2020

By using text mining, we explore the factors responsible for an increase in SUV preference. We collected 32,679 posts related to SUVs from "Bobaedream," the largest online automobile community in South Korea, and applied the LDA topic model. While previous studies have explained the SUV boom as an individual's risk aversion strategy from crime, the result shows that the topic of 'Safety' appears to be an important factor in the SUV discourse in the context of a car accident and high-speed driving situation. To conclude, the consumption of SUVs in Korean society serves as a mean to prevent anxiety and danger to individuals when driving. We insist that decreasing social trust, caused by an increase in inequality, underlies the perception of risk on the road.