• Journal of Digital Convergence
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• v.13 no.12
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• pp.161-169
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• 2015

Realtime search words of typical portal sites appear every few seconds in descending order by search frequency in order to show issues increasing rapidly in interest. However, the characteristics of realtime search words reordering within too short a time cause problems that they go over the key issues of the day. This paper proposes a method for deriving a key issue through association analysis of realtime search words. The proposed method first makes scores of realtime search words depending on the ranking and the relative interest, and derives the top 10 search words through descriptive statistics for groups. Then, it extracts association rules depending on 'support' and 'confidence', and chooses the key issue based on the results as a graph visualizing them. The results of experiments show that the key issue through association rules is more meaningful than the first realtime search word.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• The Korean Journal of Archival Studies
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• no.29
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• pp.129-173
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• 2011

Standards-curriculum based archival contents for education is the best effective teaching and learning units for historical thinking abilities. This paper purposes a developing procedures of an archival contents for education that is theoretical instructions of developing an archival contents for education by the National Archives of Korea. This paper can be used of the theoretical bases for the National Archives of Korea by proposing the methodology of development of an archival contents for education. The developing procedures of an archival contents for education is the same with the procedures of developing an e-learning contents that has planning, analyzing, designing, developing and assessing steps but it is characterized by an archival contents for education that is curriculum standards analysis, collection analysis, and detailed design for structured formats in effective-accomplishments for teaching-learning objectives. I propose the procedures for determining teaching-learning subjects that enable the development of an archival contents for education by curriculum standards analysis. I also propose the procedures for deriving the key words from the teaching-learning subjects. Collection analysis methods analyze key records that correspond to the learning subjects according to the selection criteria of primary sources. In the steps of designing, titles of contents and contents structures have to be determined and storyboards based on flowchart of learning have to be made of according to the results of analyses. In the steps of developing contents, making a copy of primary sources like a original is the key points. And also in the steps of assessment, products of teaching-learning contents to effectively achieve the teaching-learning objectives have to be estimated by the appraisal board. Finally I propose that user's survey research after the services have to be reflected on contents updates and new developments of contents.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.275-294
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• 2019

In order to understand the recent trends in mathematics education research papers, data mining method was applied to analyze journals of the mathematics education posterior to the year of 2016. Text mining method is useful in the sense that it utilizes statistical approach to understand the linkages and influencing relationship between concepts and deriving the meaning that data shows by visualizing the process. Therefore, this research analyzed the key words largely mentioned in the recent mathematics education journals. Also the correlation between the subjects of mathematics education was deduced by using topic modeling. By using the trend analysis tool it is possible to understand the vital point which researchers consider it as important in recent mathematics education area and at the same time we tried to use it as a fundamental data to decide the upcoming research topic that is worth noticing.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.