• Women's Health Nursing
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• v.29 no.2
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.3
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• pp.556-571
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• 2020

This study predicts consumer preference for social clothing at work, excluding uniforms using the self-product congruence theory that also establishes a model to predict the preference for recommended products that match the consumer's own image. A total of 490 Korean male office workers participated in this study. Participants' self-image and the product images of 20 apparel items were measured using nine adjective semantic scales (namely elegant, stable, sincere, refined, intense, luxury, bold, conspicuous, and polite). A model was then constructed to predict the consumer preferences using a neural network with Python and TensorFlow. The resulting Predict Preference Model using Product Image (PPMPI) was trained using product image and the preference of each product. Current research confirms that product preference can be predicted by the self-image instead of by entering the product image. The prediction accuracy rate of the PPMPI was over 80%. We used 490 items of test data consisting of self-images to predict the consumer preferences for using the PPMPI. The test of the PPMPI showed that the prediction rate differed depending on product attributes. The prediction rate of work apparel with normative images was over 70% and higher than for other forms of apparel.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.33-43
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• 2012

Semantic web is utilized to construct advanced information service by using semantic relationships between entities. Text mining can be applied to generate semantic relationships from unstructured data resources. In this study, ontology schema guideline, ontology instance generation, disambiguation of same name by text mining and advanced historical people finding service by reasoning have been proposed. Various relationships between historical event, organization, people, which are created by domain experts, are linked to literatures of National Institute of Korean History (NIKH). It improves the effectiveness of user access and proposes advanced people finding service based on relationships. In order to distinguish between people with the same name, we compares the structure and edge, nodes of personal social network. To provide additional information, external resources including thesaurus and web are linked to all of internal related resources as well.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.

• The Journal of Korean Academic Society of Nursing Education
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• v.28 no.4
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• pp.400-410
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• 2022

Purpose: This study aimed to understand the keywords and major topics of the educational goals and objectives of nursing educational institutions in South Korea. Methods: From May 10 to May 20, 2022, the educational goals and objectives of all 201 nursing educational institutions in South Korea were collected. Using the NetMiner program, degree and degree centrality, semantic structure, and topic modeling were analyzed. Results: The top keywords and semantic structures of educational goals included 'respect for human (life)-spirit-science-based on, global-competency-professional nurse-nursing personnel-training, professional-science-knowledge-skills, and patients-therapeutic care-relationship.' The educational goals' major topics were clients well-being based on science and respect for human life, a practicing nurse with capabilities and spirit, fostering a nursing personnel with creativity and professionalism, and training of global nurses. The top keywords and semantic structures of the educational objectives included 'holistic care-nursing-research-action-capability, critical thinking-health-problem solving-capability, and efficiency-communication-collaboration-capability.' The educational objectives' major topics were 'nursing professionalism, communication and problem-solving capability; a change of healthcare environments and a progress of nursing practices; fostering professional nurses with creativity and global capability; and clients' health and nursing practice.' Conclusion: Educational goals in nursing presented specific nursing values and concepts, such as respect for human life, therapeutic care relationships, and the promotion of well-being. Educational objectives in nursing presented the competencies of nurses as defined by the Korean Accreditation Board of Nursing Education (KABONE). Recently, the KABONE announced new program outcomes and competencies, which will require the revision of educational goals. To achieve those educational objectives, it is suggested that the expected level of competencies be clearly defined for nursing graduates.

• Journal of Korean Elementary Science Education
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• v.39 no.3
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• pp.399-411
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• 2020

This study aims to explore how elementary students perceive and approach the issue of plastic debris in marine habitats by examining students' perspectives on the ecosystem and environmental solutions. The study was conducted to 143 Grade Four elementary school students in Seoul. After implementing two class-units on plastic waste, students' constructed responses on the problem of and solutions to plastic debris in marine habitats were collected. Data were analyzed through semantic network analysis and the keywords were visualized to reflect their relationships. Furthermore, students' responses on how they perceive environmental problems were further analyzed based on the following analysis criteria: students' perspectives on the ecosystem, the level of complexity of food chain(s), and the scope of their perspective. Also, student responses on environmental solutions were classified to be either at a personal or social level. Through semantic network analysis, keywords identified for students' perceptions on the problem were the sea, plastic, debris, animals, living things, humans, extinction, while keywords extracted for the solutions were plastic, debris, recycling, disposable, and I. Based on the analysis criteria, it was found that students were well aware of the food chain concept, could perceive the ecosystem as having comprised of both biotic and abiotic factors, and could approach the problem beyond the scope of the marine environment. Also, most students mentioned the solutions only at a personal level. Based on the findings, implications on how to move forward in educating environmental issues related to the ecosystem in science education is further discussed.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.307-314
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• 2019

This paper was intended to identify the knowledge structure of youth-related research by looking at the research trends of research papers published in Korean Journal of Youth Studies from 2010 to 2018. Using keywords extracted from the papers, the Centrality and Cohesion analysis of the keyword network analysis of the NetMiner program were used. In the analysis of degree centrality, the "relationship" was the highest, followed by schools and youth, and high in the order of parents and violence. In the analysis of betweenness centrality, the "relationship" was also the highest, followed by youth, school, need, education, parents, children, abuse/emotion(the same level), institutions, regions, cell phones/prevention/welfare(the same level), elementary, attachment, suicide, addiction, society, violence, children, services, support, policy/teachers(the same level). According to the cohesion analysis, school life and policy, addiction, parent & peer relations, civic education & welfare support, sentiment and thinking, college, abuse & suicide were divided into a total of seven sub-topic subjects.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.553-566
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• 2022

This study collects social data and academic research data from portal sites and RISS, and analyzes TF-IDF, N-Gram, semantic network analysis, and CONCOR analysis to analyze the social awareness and current aspects of 'AI Literacy' and 'AI Convergence Education'. Through this, we tried to understand the social awareness aspect and the current situation, and to suggest implications and directions. In the social data, the collection of 'AI Convergence Education' was more than twice that of 'AI Literacy', indicating that awareness of 'AI Literacy' was relatively low. In 'AI Literacy', the keyword 'human' in social data showed no cluster to which it belonged, indicating a lack of philosophical interest in and awareness of humanities and AI. In addition, the keyword 'Ministry of Education' showed high frequency, importance, and centrality of connection only in the social data of 'AI convergence education', confirming that 'AI convergence education' is closely related to government policy.

• 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.

• Korean Journal of Biological Psychiatry
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• v.23 no.3
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• pp.89-101
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• 2016

Transcranial magnetic stimulation (TMS) is a safe, noninvasive and useful technique for exploring brain function. Especially, for the study of cognition, the technique can modulate a cognitive performance if the targeted area is engaged, because TMS has an effect on cortical network. The effect of TMS can vary depending on the frequency, intensity, and timing of stimulation. In this paper, we review the studies with TMS targeting various regions for evaluation of cognitive function. Cognitive functions, such as attention, working memory, semantic decision, discrimination and social cognition can be improved or deteriorated according to TMS stimulation protocols. Furthermore, potential therapeutic applications of TMS, including therapy in a variety of illness and research into cortical localization, are discussed.