• Smart Media Journal
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• v.11 no.10
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• pp.54-64
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• 2022

A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation.

• 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 the Korean Society of Safety
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• v.37 no.3
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• pp.9-15
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• 2022

The analysis of the causes of accidents in workplaces where machines and tools are used is essential to improve the effectiveness and efficiency of safety prevention policies in places of employment in Korea. The causes of workplace accidents are not fully understood mainly due to difficulties in analyzing available descriptive information. This study focuses on the automated accident cause analysis in workplaces based on the accident abstracts found in industrial accident reports written in an unstructured descriptive format. The method proposed in this paper is based on text data mining and uses the keyword search function of Excel software to automate the analysis. The analysis results indicate that the primary reason for the frequency of accidents is related to technical aspects at a stage in which dangerous situations occur in the workplace. Accidents due to managerial causes are typically observed when danger exists in the workplace; however, managerial actions play a more important role in reducing accident severity. A small company tends to use unsafe machines and devices, leading to further accidents due to technical causes, whereas managerial causes are more conspicuous as the company grows. To preclude the occurrence of accidents due to inadequate knowledge, the implementation of safety management and the provision of safety education to elderly workers at the early stage of their employment are particularly important for small companies with less than 100 workers.

• Atmosphere
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• v.30 no.3
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• 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.

• Knowledge Management Research
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• v.21 no.2
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• pp.177-196
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• 2020

Since the adoption of K-IFRS(Korean International Financial Reporting Standards), the amount of financial footnotes has been increased. However, due to the stereotypical phrase and the lack of conciseness, deriving the core information from footnotes is not really easy yet. To propose a solution for this problem, this study tried financial footnote analysis for financial ratio predictions based on text-mining techniques. Using the financial statements data from 2013 to 2018, we tried to predict the earning per share (EPS) of the following quarter. We found that measured prediction errors were significantly reduced when text-mined footnotes data were jointly used. We believe this result came from the fact that discretionary financial figures, which were hardly predicted with quantitative financial data, were more correlated with footnotes texts.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.151-159
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• 2015

This article analyzes English abstracts of the articles published in Journal of the Korean Data & Information Science Society using text mining techniques. At first, term-document matrices are formed by various methods and then visualized by social network analysis. LDA (latent Dirichlet allocation) and CTM (correlated topic model) are also employed in order to extract topics from the abstracts. Performances of the topic models are compared via entropy for several numbers of topics and weighting methods to form term-document matrices.

• Korean Journal of Construction Engineering and Management
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• v.21 no.2
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• pp.15-21
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• 2020

The relationship between keywords, found in all productivity related papers published in the KSCE journal for last 15 years, were analyzed in order to reveal a research trend in the area using text mining and A-Priori algorithm. As the results, it is found that the word of 'productivity' is most closely related to the words of 'work' and 'labor'. Futhermore, the word is somewhat related to those of 'factor', 'model', simulation', and 'work time'. It is also revealed that, on the other hand, the words of 'machine' and 'equipment' have little relationships with the keyword. This research will be a great help for academia to understand a research trend in the area of construction productivity.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.23-30
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• 2020

This study used the text mining method to analyze the research trend of the Journal of Creative Information Culture(JCIC) which is the journal of convergence. The existing research trend analysis method has a limitation in that the researcher's personality is reflected using the traditional content analysis method. In order to complement the limitations of existing research trend analysis, this study used topic modeling. The English abstract of the paper was analyzed from 2015 to 2019 of the JCIC. As a result, the word that appeared most in the JCIC was "education," and eight research topics were drawn. The derived subjects were analyzed by educational subject, educational evaluation, learner's competence, software education and maker culture, information education and computer education, future education, creativity, teaching and learning methods. This study is meaningful in that it analyzes the research trend of the JCIC using text mining.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.59-73
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• 2009

Keyword extraction is an important and essential technique for text mining applications such as information retrieval, text categorization, summarization and topic detection. A set of keywords extracted from a large-scale electronic document data are used for significant features for text mining algorithms and they contribute to improve the performance of document browsing, topic detection, and automated text classification. This paper presents a keyword extraction technique that can be used to detect topics for each news domain from a large document collection of internet news portal sites. Basically, we have used six variants of traditional TF-IDF weighting model. On top of the TF-IDF model, we propose a word filtering technique called 'cross-domain comparison filtering'. To prove effectiveness of our method, we have analyzed usefulness of keywords extracted from Korean news articles and have presented changes of the keywords over time of each news domain.