• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.153-161
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• 2018

In this paper, we propose a text-centered approach to identify the research trend of thyroid cancer in Korea. We incorporate statistical analysis, text mining and machine learning techniques with our clinical insights to find connective associations between terminologies and to discover informative clusters of literatures. The incidence of thyroid cancer in Korea increased rapidly in the 2000s, which fueled the debate regarding overdiagnosis, but recently the number of patients undergoing surgery has decreased significantly due to conscious reform efforts from various circles. We analyzed the abstracts and keywords of related research papers from DBpia. It was found that most were case reports in the 1980s, and some papers in the 1990s discussed the early detection of thyroid cancer by mass screening. While many papers focused on different diagnostic techniques and the detection of small cancers in the 2000s, many emphasized more on the quality of life of patients in the 2010s. There was an apparent change in the topics of thyroid cancer research over past decades. The results of this study would serve as a reference guide for current and future research directions.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.83-89
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• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.235-243
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• 2022

This paper aims to find promising export items for market expansion of defense export items. Germany, the UK, and France were selected as export target countries to obtain unstructured forecast data on weapons system acquisition plans for the next ten years by each country. Using the TF-IDF in text mining analysis, keywords that appeared frequently in data from three countries were derived. As a result of this paper, keywords for each country's major acquisition projects drawing. However, most of the derived keywords were related to mainstay weapon systems produced by domestic defense companies in each country. To discover promising export items from text mining, we proposed that the drawn keywords are distinguished as similar weapon systems. In addition, we assort the weapon systems that the three countries will get a plan to acquire commonly. As a result of this paper, it can be seen that the current promising export item is a weapon system related to the information system. Prioritizing overseas demands using key words can set clear market entry goals. In the case of domestic companies based on needs, it is possible to establish a specific entry strategy. Relevant organizations also can provide customized marketing support.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.302-310
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• 2022

This study examined the results obtained using the text mining method for research trends related to learning counseling among adolescents and suggested subsequent research directions. The top 1 and 2 of Korean youth concerns are learning and career paths. Topic modeling analysis was conducted using text mining techniques that can minimize researcher's subjectivity and prejudice for 201 academic papers above KCI registration candidates through RISS with keywords such as Learning Counseling and Academic Counseling. Learning counseling topic results showed counseling experience [topic 1], group counseling research [topic 2], parent counseling [topic 3], and learning technology program development [topic 4]. Research related to learning counseling is developing counseling for emotional stability. Group counseling, parent counseling, and learning technology programs. Learning counseling to solve adolescents' concerns is expected to continue research on integrated support through psychological emotion, parent counseling, and collaboration with learning technology experts.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.160-165
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• 2021

In order to identify and analyze potential aviation safety hazards, analysis of aviation safety report data must be preceded. Therefore, in consideration of the provisions of the Aviation Safety Act and the recommendations of ICAO Doc 9859 SMM Edition 4th, personal information in the reporting data and sensitive information of the reporter, etc. It identifies the scope of de-identification targets and suggests a method for applying de-identification processing technology to personal and sensitive information including unstructured text data.

• Korean Journal of Childcare and Education
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• v.16 no.6
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• pp.253-272
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• 2020

Objective: This study aimed to analyze the counseling contents of daycare center teachers by using text mining and semantic network analysis methods to find the necessary support directions for daycare teachers and to improve the quality of child-care. Methods: Five hundred thirteen cases of counseling recorded on the open bulletin board of online counseling (Naver Bands for Nursery Teacher Counseling) were collected, and frequency analysis, centrality solidarity analysis, and machine learning-based topic analysis were conducted using the NetMiner4.3 program. Results: First, 'teacher-to-child ratio' was highest in the frequency. Second, 'colleagues' were all high in all centrality analysis. Third, machine learning-based topical analysis shows that the topics were categorized as subjects about 'childcare and education', 'working environment that supports professional development' and 'working condition', and among them, 'first-time teacher concerns' accounted for 44% of the total counseling content. Conclusion/Implications: This study implied that it is necessary to provide high-quality child-care and education to infants by lowering the 'teacher-to-child ratio', and a systematic program is needed to help improve effective communication skills in interpersonal relationships such as between parents, fellow teachers, and principals. In addition, self-development and efforts to improve teachers expertise should be prioritized in order to improve infant care quality and quality of teachers.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.147-159
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• 2022

Nowadays, various social media platforms are widely spread and people closely use such platforms in daily life. By doing so, social influencers with a large number of subscribers, views, and comments have huge impact in our society. Following this trend, many companies are actively using influencers for marketing purpose to promote their products and services. In this study, we extract the speeches of influencers from videos for game marketing and analyze them using various text mining techniques. In the analysis, we distinguish game videos leading to successful marketing and failed marketing, and we explore and compare the linguistic features of the influencers for successful and failed marketings.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.729-742
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• 2023

This two-part study explores the changes in the types of perceived value and consumption channels for vintage clothing and the relationship between the two variables. In Study 1, we used text mining with the keyword "fashion+vintage." Emotional value was the most frequently mentioned, and environmental value increased the most. We also revealed an increasing trend in online channels for vintage clothing consumption. In Study 2, we analyzed 30 interviews with consumers who had purchased vintage clothing through online channels. We identified 7 attributes and 20 goals for vintage consumption online and pinpointed three strong connections. First, consumers reported high levels of service satisfaction due to the usefulness of algorithms. Second, the authenticity and heritage information available through online and mobile channels were associated with consumers' perceptions of value related to financial benefits. Third, consumers sought to find rare products through online channels, leading to a strong influence on their sense of achievement. Overall, this study proposed ways to increase the value of vintage clothing perceived by consumers through consumption online.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.403-418
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• 2023

Purpose: The acceleration of technological innovation, specifically Industry 4.0, has triggered the emergence of a quality management paradigm known as Quality 4.0. This study aims to provide a systematic overview of dispersed studies on Quality 4.0 across various disciplines and to stimulate further academic discussions and industrial transformations. Methods: Text mining and machine learning approaches are applied to learn and identify key research topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview of Quality 4.0. Results: 1) A total of 27 key research topics were identified based on the analysis of 1234 research papers related to Quality 4.0. 2) A relationship among the 27 key research topics was identified. 3) A multilevel framework consisting of technological enablers, business methods and strategies, goals, application industries of Quality 4.0 was developed. 4) The trends of key research topics was analyzed. Conclusion: The identification of 27 key research topics and the development of the Quality 4.0 framework contribute to a better understanding of Quality 4.0. This research lays the groundwork for future academic and industrial advancements in the field and encourages further discussions and transformations within the industry.

• Human Ecology Research
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• v.59 no.1
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• pp.23-43
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• 2021

This study looked through the text mining analysis to check the status of the virtual assistant service, and explore the needs of consumers, and present consumer-oriented directions. Trendup 4.0 was used to analyze the keywords of AI services in Online News and social media from 2016 to 2020. The R program was used to collect consumer comment data and implement Topic Modeling analysis. According to the analysis, the number of mentions of AI services in mass media and social media has steadily increased. The Sentimental Analysis showed consumers were feeling positive about AI services in terms of useful and convenient functional and emotional aspects such as pleasure and interest. However, consumers were also experiencing complexity and difficulty with AI services and had concerns and fears about the use of AI services in the early stages of their introduction. The results of the consumer review analysis showed that there were topics(Technical Requirements) related to technology and the access process for the AI services to be provided, and topics (Consumer Request) expressed negative feelings about AI services, and topics(Consumer Life Support Area) about specific functions in the use of AI services. Text mining analysis enable this study to confirm consumer expectations or concerns about AI service, and to examine areas of service support that consumers experienced. The review data on each platform also revealed that the potential needs of consumers could be met by expanding the scope of support services and applying platform-specific strengths to provide differentiated services.