• Journal of Korean Home Economics Education Association
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• v.31 no.4
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• pp.1-18
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• 2019

The purpose of this study was to analyze the research trend in home economics education using network text analysis method. The 586 research articles published in the Journal of Home Economics Education Association between July, 2003 and December 2018 were examined using Neckinger 4, a social network analysis software. The frequency and centrality measures(degree centrality, closeness centrality, and betweenness centrality) were calculated for the words appeared throughout the whole period, and the centrality analysis and LAD(Latent Dirichlet Allocation) were conducted for the four sub-periods. The results are as follows: first, the most frequently appeared words are parents, culture, unit, health, career, consumption, practicality, etc. The words such as parents and management scored high in degree centrality; parents and male students in closeness centrality; and male students and units in betweenness centrality. Second, when divided into four periods, the words such as education, family, purpose, class, middle school, and school appeared most frequently across the periods; but some words such as 'purpose' (in period 3 and 4), or 'process' (in period 4) were salient only in certain periods. Third, the words with high centrality were consistent regardless of the types of centrality within each period. Fourth, the topic analysis using LAD showed that curriculum, textbook, family healthiness, teaching-learning, evaluation, dietary life, appearance management, and consumption were the topics consistently appeared across all periods. The topics have become diversified and deepened. New topics such as teacher training and safety appeared in later periods, possibly due to the curriculum and national policy changes, and housing as a less represented topic is suggested as an area that needs further research attention. This study has implication in that it allows researchers to identify the major research interests and the trends in research by researchers in home economic education.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.1
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• pp.110-119
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• 2009

Sosang Pal-Kyung(瀟湘八景), which originated in China and means eight scenes of So River and Sang River, greatly influenced the poems and the pictures in East Asia for a long time and became a cultural phenomenon leading the stereotype of the traditional landscapes in Korea and Japan. Studies on 'Kyung(a scene)' such as 'Pal-Kyung(八景)', have been made continuously until now, but there are no results of a study intensively focusing on the meaning and the form of Sosang Pal-Kyung, which is the origin of the domestic Pal-Kyung culture. The goal of this study is to investigate the typical form observed in Sosang Pal-Kyung-Ga(瀟湘八景歌) and Sosang Pal-Kyung-Do(瀟湘八景圖), as a text of a cultural landscape, and to clear up the coherence structure between a recognition system and a way of thinking that existed in the cultural phenomenon of Sosang Pal-Kyung. In this study, the symbolism of Pal(八) was summarized and the surface structure and the correlation of each Kyung of Sosang Pal-Kyung was explained in light of semiotics through segmenting and disjointing the lexeme of a landscape while the coherence structure and the meaning of Sosang Pal-Kyung-Ga and Sosang Pal-Kyung-Do as a text were investigated. Sosang Pal-Kyung is based on the view of the Sun and the Moon(or Positive and Negative) and the Eight Trigrams(八卦) for divination and is a linguistic symbol in which human life and the principle of circulation and conversion of nature are expressed as characters and picture texts. Its structure has strong coherence and cohesion, which attempt to move the abstruse truth of nature into human consciousness by developing and corresponding the grammatical structure and form of the sentences and the implicative languages emphasizing the symbolism of the words to the characteristics of similarities and contrast. In addition, Sosang Pal-Kyung expresses human life, the processes of birth and death of nature and the mutual response dialectically by putting various factors of the landscape in the frame of regular formality and structure. It is considered that the image signs in Sosang Pal-Kyung emphasize the theory of circulation of human life and nature are narrative scenery, which one looks at with a contemplative view in the circulation system of the time and the season. The cultural phenomena of Sosang Pal-Kyung in the Joseon Dynasty, which had been handed down from the Goryeo Dynasty, had become the driving force of leading aesthetics of Joseon's art and literature by adding the scenery of the point of view of Sung Confucianism. Its coherence structure was changed, but its cohesion was handed down continuously so that it became not only the basic text of the traditional and cultural landscape but also, the typical Korean-style stereotype of a landscape.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.61-73
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• 2021

This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji·Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji·Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.75-100
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• 2022

Untact mobile commerce shows a rapid growth due to the prolonged COVID-19 pandemic. And companies have a lot of tough competition in this trend. However, the detail pages of products which play an important role in purchase decision have been provided mostly for consumers in a form of stereotyped information composition. This study has found that the form of (image-centered vs. text-centered) information composition of detailed descriptions of products in the detail pages of mobile products has an effect on product attitude and purchase intention as consumers' information appeal methods vary depending on product types (search goods vs. experience goods). That is, search goods whose information search is easy and whose quality is predictable could be found that product attitude and purchase intention have a more positive effect on the form of image-centered information composition. And experience goods whose quality is unpredictable could be found that product attitude and purchase intention have a more positive effect on the form of text-centered information composition. And effects of congruence between product types based on Higgins' regulatory focus theory and the form of information composition have found to vary depending on consumers' chronic regulatory focus. Promotion focus seeking consumers showed effects of congruence between product types and the form of information composition and prevention focus seeking consumers did not show effects of congruence between them. That is, promotion focus seeking consumers have found to have more positive product attitude and purchase intention in the form of image-centered information composition of experience goods and text-centered information composition of search goods. And prevention focus seeking consumers have found to be unable to have an effect on product attitude and purchase intention even though the form of image or text-centered information composition of search and experience goods is presented. The study implies that the form of information composition should be designed, produced, and provided for consumers by considering product types and consumer propensity when designing it in the detail pages of mobile products.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.57-66
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• 2017

Lately, academic interests in big data analysis and social network has been prominently raised. Various academic fields are involved in this social network based research trend, which is, social network has been actively used as the research topic in social science field as well as in natural science field. Accordingly, this paper focuses on the text analysis and the following social network analysis with the Master's and Doctor's dissertations. The result indicates that certain words had a high frequency throughout the entire period and some words had fluctuating frequencies in different period. In detail, the words with a high frequency had a higher betweenness centrality and each period seems to have a distinctive research flow. Therefore, it was found that the subjects of the Master's and Doctor's dissertations were changed sensitively to the development of IT technology and changes in information curriculum of elementary, middle and high school. It is predicted that researches related to smart, mobile, smartphone, SNS, application, storytelling, multicultural, and STEAM, which had an increased frequency in period 4, would be continuously conducted. Moreover, the topics of robots, programming, coding, algorithms, creativity, interaction, and privacy will also be studied steadily.

• Journal of the Korean Society for information Management
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• v.28 no.2
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• pp.53-63
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• 2011

This paper aims to find out the kinds of sub-topics that were researched in relation to Information Literacy (IL). The text mining method was applied to the articles with information literacy' in the fields of the descriptor, title and in the LISA Database. Also, out of 214 journals that published these articles, those with the top ten highest frequencies were listed and analyzed. Research results show that 908 articles on information literacy were published in 214 journals and User training' and Students' were major descriptors in the sub-topic area of information literacy. Also, Reference Services Review and The Journal of Academic Librarianship are two key journals in IL research as they have the highest frequency of related articles and have shown increasing trends.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.77-93
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• 2014

Kinetic typography has been suggested to express emotions in computer-mediated communication (CMC) by empowering static texts with dynamic attributes, where conversations occur primarily in text-based forms. In this work, we investigate whether pre-defined kinetic typography effects are capable of delivering emotions, and further, which specific attributes of kinetic typography arouse such emotions. The results show that emotional response of users were corresponding to the emotions intended by experts, indicating that pre-defined kinetic typography is an applicable way to express emotions consistently in CMC. Also, results demonstrate some key attributes that derive certain levels of mood and energy respectively. Energy level turned out to be affected by the font size, transparency, direction of movement, amount of movement, velocity, and acceleration of the text, while mood level was influenced by the transparency, direction of movement, regularity in movement, and speed of the text.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.93-107
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• 2004

This study is concerned with proposing a new construct named "spatial metaphor" in the field of user interface design for web. Recently, web has been recognized as an important vehicle of delivering messages and information to customers. Since both hyperlink and multimedia technology are crucial part of web, its user interface requires a new approach to enhance user's acceptance of web. In this sense, we introduced a new concept named "spatial metaphor" instead of hierarchical menus. As a theoretical basis, Davis (1986)'s TAM(Technology Acceptance Model) was used to test the statistical validity of the proposed spatial metaphor. For test web site, we developed a prototype designed by using atomic-web system and spatial metaphor. By using the prototype, we built a web-based questionnaire system so that respondents can use it directly before answering the questionnaire. To prove its statistical validity, we collected valid questionnaires and tested with LISREL. In this way, statistical validity of our proposed approach was proven.approach was proven.