• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.17-30
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• 2015

Technology-oriented national R&D programs produce intellectual property as their final result. Patents, as typical industrial intellectual property, are therefore considered an important factor when evaluating the outcome of R&D programs. Among the main components of patent evaluation, in particular, the patent right quality is a key component constituting patent value, together with marketability and usability. Current approaches for patent right quality evaluation rely mostly on intrinsic knowledge of patent attorneys, and the recent rapid increase of national R&D patents is making expert-based evaluation costly and time-consuming. Therefore, this study defines a hierarchy of patent right quality and then proposes how to quantify the evaluation process of patent right quality by combining text mining and regression analysis. This study will contribute to understanding of the systemic view of the patent right quality evaluation, as well as be an efficient aid for evaluating patents in R&D program assessment processes.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.729-734
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• 2022

Various studies using accident cases are being conducted to identify the causes of accidents in the construction industry, but studies on the differences between public and private construction are insignificant. In this study, web scraping and text mining technologies were applied to analyze the causes of accidents by order type. Through statistical analysis and word cloud analysis of more than 10,000 structured and unstructured data collected, it was confirmed that there was a difference in the types and causes of accidents in public and private construction. In addition, it can contribute to the establishment of safety management measures in the future by identifying the correlation between major accident causes.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.859-865
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• 2023

In a situation where service providers are increasingly focusing on content development due to the intense competition in the OTT market, interactive content that encourages active participation from viewers is garnering significant attention. In response to this trend, research on interactive content is being conducted more actively. This study aims to analyze interactive content through text mining techniques, with a specific focus on online unstructured data. The analysis includes deriving the characteristics of keywords according to their weight, examining the relationship between OTT platforms and interactive content, and tracking changes in the trends of interactive content based on objective data. To conduct this analysis, detailed techniques such as 'Word Cloud', 'Relationship Analysis', and 'Keyword Trend' are used, and the study also aims to derive meaningful implications from these analyses.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.37-47
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• 2024

The food tech market, which uses artificial intelligence robots for the restaurant industry, is gradually expanding. Among them, the robot barista, a representative food tech case for the restaurant industry, is characterized by increasing the efficiency of operators and providing things for visitors to see and enjoy through a 24-hour unmanned operation. This research was conducted through text mining analysis to examine trends related to robot baristas in the restaurant industry. The research results are as follows. First, keywords such as coffee, cafe, certification, ordering, taste, interest, people, robot cafe, coffee barista expert, free, course, unmanned, and wine sommelier were highly frequent. Second, time, variety, possibility, people, process, operation, service, and thought showed high closeness centrality. Third, as a result of CONCOR analysis, a total of 5 keyword clusters with high relevance to the restaurant industry were formed. In order to activate robot barista in the future, it is necessary to pay more attention to functional development that can strengthen its functions and features, as well as online promotion through various events and SNS in the robot barista cafe.

• Journal of Fashion Business
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• v.28 no.2
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• pp.139-151
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• 2024

In recent years, consumer clothing behaviors have undergone significant changes due to global phenomena such as climate change, pandemics, and advances in IT technology. Laundry behaviors closely connected to how consumers handle clothes and their clothing lifecycle have also experienced considerable transformations. However, research on laundry behavior has been limited despite its importance in understanding consumer clothing habits. This study employed text mining analysis of social data spanning the past decade to explore overall trends in consumer laundry behavior, aiming to understand key topics of interest and changes over time. Through LDA topic modeling analysis, nine topics were identified. They were grouped into subjects, targets, methods, and reasons related to laundry. Analyzing relative frequencies of keywords for each topic group revealed evolving consumer laundry behavior in response to societal changes. Over time, laundry behavior showed a dispersal of agents and locations, increased diversification of laundry targets, and a growing interest in various methods and reasons for doing laundry. This research sheds light on the broader context of laundry behavior, offering a more comprehensive understanding of consumer attitudes and perceptions than previous studies. It underscores the significance of laundry as a daily, socio-cultural aspect of our lives. Additionally, this study identifies changing customer values and suggests improvements and strategic branding for laundry services, providing practical implications.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.17-27
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• 2021

The aim of this research was to identify the issue of the work permit system and consciousness of the people on the system, and to suggest some ideas on the government policies on it. To achieve the aim of research, this research used text mining based on social data. This research collected 1,453,272 texts from 6,217 units of online documents which contained 'work permit system' from January to December, 2020 using Textom, and did text-mining and social network analysis. This research extracted 100 key words frequently mentioned from the analyses of data top-level key word frequency, and degree centrality analysis, and constituted job problem, importance of policy process, competitiveness in the respect of industries, and improvement of living conditions of foreign workers as major key words. In addition, through semantic network analysis, this research figured out major awareness like 'employment policy', and various kinds of ambient awareness like 'international cooperation', 'workers' human rights', 'law', 'recruitment of foreigners', 'corporate competitiveness', 'immigrant culture' and 'foreign workforce management'. Finally, this research suggested some ideas worth considering in establishing government policies on the work permit system and doing related researches.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.543-550
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• 2019

In 2006, Janet Wing defined computational thinking and operated SW education as a formal curriculum in the UK in 2013. This study collected related research papers by using computational thinking, which has recently increased in importance, and analyzed it using text mining. In the first, CONCOR analysis was conducted with the keyword of computational thinking. In the second, text mining of the components of computational thinking was selected by the repr23esentative academic journals at domestic and foreign. As a result of the two-time analysis, first, abstraction, algorithm, data processing, problem decomposition, and pattern recognition were the core of the study of computational thinking component. Second, research on convergence education centered on computational thinking and science and mathematics subjects was actively conducted. Third, research on computational thinking has been expanding since 2010. Research and development of the classification and definition of computational thinking and components and applying them to education sites should be conducted steadily.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.275-287
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• 2014

Recently, the volume of unstructured text data generated by various social media has been increasing rapidly; consequently, the use of text mining to support decision-making has also been growing. In particular, academia and industry are paying significant attention to topic analysis in order to discover the main issues from a large volume of text documents. Topic analysis can be regarded as static analysis because it analyzes a snapshot of the distribution of various issues. In contrast, some recent studies have attempted to perform dynamic issue tracking, which analyzes and traces issue trends during a predefined period. However, most traditional issue tracking methods have a common limitation : when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. Additionally, traditional issue tracking methods do not concentrate on the transition of individuals' interests from certain issues to others, although the methods can illustrate macro-level issue trends. In this paper, we propose an individual interests tracking methodology to overcome the two limitations of traditional issue tracking methods. Our main goal is not to track macro-level issue trends but to analyze trends of individual interests flow. Further, our methodology has extensible characteristics because it analyzes only newly added documents when the period of analysis is extended. In this paper, we also analyze the results of applying our methodology to news articles and their access logs.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.40-56
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• 2022

This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package "rvest." The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. "LIS Recruitment," "LIS Issues," "Other Discussion," "LIS Education," "LIS Research," "LIS Exams," "General Information related to Library," "LIS Admission," "Library and Professional Activities," and "Information Communication Technology (ICT)." It was found that the majority of the posts belonged to "LIS Exam," followed by "Other Discussions" and "General Information related to the Library."

• Journal of Digital Convergence
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• v.17 no.4
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• pp.211-217
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• 2019

As the widespread use of mobile health intervention among Korean patients with chronic disease, it is needed to identify research trends in mobile health intervention on chronic care using text mining technique. This secondary data analysis was conducted to investigate characteristics and main research topics in intervention studies from 2005 to 2018 with a total of 20 peer reviewed articles. Microsoft Excel and Text Analyzer were used for data analysis. Mobile health interventions were mainly applied to hypertension, diabetes, stroke, and coronary artery disease. The most common type of intervention was to develop mobile application. Lately, 'feasibility', 'mobile health', and 'outcome measure' were frequently presented. Future larger studies are needed to identify the relationships among key terms and the effectiveness of mobile health intervention using social network analysis.