• Human Ecology Research
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• v.60 no.4
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• pp.625-643
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• 2022

The purpose of this study was to explore the trends and core keywords of the secondary school home economics teacher selection test using content analysis and text network analysis. The sample comprised texts of the secondary school home economics teacher 1st selection test for the 2017-2022 school years. Determination of frequency of occurrence, generation of word clouds, centrality analysis, and topic modeling were performed using NetMiner 4.4. The key results were as follows. First, content analysis revealed that the number of questions and scores for each subject (field) has remained constant since 2020, unlike before 2020. In terms of subjects, most questions focused on 'theory of home economics education', and among the evaluation content elements, the highest percentage of questions asked was for 'home economics teaching·learning methods and practice'. Second, the network of the secondary school home economics teacher selection test covering the 2017-2022 school years has an extremely weak density. For the 2017-2019 school years, 'learning', 'evaluation', 'instruction', and 'method' appeared as important keywords, and 7 topics were extracted. For the 2020-2022 school years, 'evaluation', 'class', 'learning', 'cycle', and 'model' were influential keywords, and five topics were extracted. This study is meaningful in that it attempted a new research method combining content analysis and text network analysis and prepared basic data for the revision of the evaluation area and evaluation content elements of the secondary school home economics teacher selection test.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.101-114
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• 2023

This research aimed to recognize the determinant characteristics affecting consumers' clothing fit evaluation by employing text mining and social network analysis. For this aim, we first extracted text data linked to clothing fit from 2,000 consumer reviews collected from social network services and conducted semantic network examination and CONCOR analysis. As a result, we reported that "pants" and "skirts" were the most commonly associated clothing items with consumers' clothing fit evaluation. And the length of clothing was most commonly investigated. Then, the "waist" and "hip" were the most critical body parts affecting consumers' perception of clothing fit. Further, the four keywords including "wide," "large," "short," and "long" were the most employed ones in consumer reviews when evaluating clothing fit. This study is meaningful in that it specifically recognized the structural relationship and semantic meanings of keywords relevant to consumers' evaluation of clothing fit, which could bring empirical reference information for advanced clothing fit.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.329-343
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• 2004

With advancement of computer and network, world wide web(WWW) as a medium of information communication is generalized in many fields. In educational aspect, applications of WWW as alternative media for class teachings or printed matters are increasing. In this article, we demonstrate a web-based electronic text on the 'probability and statistics' which is one of six fields of mathematics in the 7th curriculum. This text places importance on comprehension of concepts of probability and statistics as an applied science.

• Educational Technology International
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• v.13 no.1
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• pp.49-77
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• 2012

The development of mobile devices and network technology is changing the ways in which people communicate with one another. Mobile text message has emerged as one of the most frequently used form of communication, which also gave rise to various non-verbal texts such as emoticons. Nonetheless, the use of text messages has largely been denied in education because text messages often involve colloquial and non-verbal texts considered inappropriate or grammatically incorrect by the teacher. In efforts to provide a theoretical framework to better understand mobile e-text communication, this research compared the practical usages of non-verbal texts in the mobile e-learning environment. The study developed three types of text messages according to the degree of using non-verbal texts and their phraseology as instructors' messages, which were then distributed to 259 students via mobile text messaging. The perceptions of students were analyzed using a semantic differential scale and a questionnaire. The results showed clear differences in students' perceptions of non-verbal text and traditional text, and that optimally designed non-verbal texts turned out to encourage the students' interaction the most out of the three types of text messages. Following the discussion of the results, an expanded theoretical framework beyond Ong's concepts of orality and literacy is also suggested to understand the evolution of mobile e-text communication in education.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.59-71
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• 2018

Social media has immense popularity among all services today. Data from social network services (SNSs) can be used for various objectives, such as text prediction or sentiment analysis. There is a great deal of Korean and English data on social media that can be used for sentiment analysis, but handling such huge amounts of unstructured data presents a difficult task. Machine learning is needed to handle such huge amounts of data. This research focuses on predicting Korean and English sentiment using deep forward neural network with a deep learning architecture and compares it with other methods, such as LDA MLP and GENSIM, using logistic regression. The research findings indicate an approximately 75% accuracy rate when predicting sentiments using DNN, with a latent Dirichelet allocation (LDA) prediction accuracy rate of approximately 81%, with the corpus being approximately 64% accurate between English and Korean.

• 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.

• 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 Engineering Education Research
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• v.26 no.5
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• pp.29-41
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• 2023

Using text network analysis, this research aimed to identify significant keywords associated with each period of the revised High School Engineering curriculum from 2009-2022 and to examine their interrelationships in order to analyse the observed changes. The results of this study can be summarised as follows. Firstly, a significant increase in the number of words was observed throughout the curriculum revisions, with prominent occurrences of terms such as 'engineering', 'understanding', 'problem', 'solution', 'learning', 'evaluation' and 'diversity'. Secondly, network analysis and examination of connection centrality for each subject revealed the connection relationship that represented distinct subject characteristics. Thirdly, the study of the engineering curriculum revealed shifts in emphasised content with each revision. Based on these findings, recommendations were formulated. Firstly, given the growing importance of engineering, it is imperative to conduct systematic research on engineering education in primary and secondary school contexts. Secondly, efforts should be made to strengthen the link between Engineering and Technogy･Home-economics subjects in secondary schools. Finally, high school engineering subjects should be used not only to explore engineering careers, but also to cultivate talents with interdisciplinary expertise.

• 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.

• Korea Trade Review
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• v.47 no.4
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• pp.137-159
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• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.