• Korean Institute of Interior Design Journal
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• v.19 no.4
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• pp.3-10
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• 2010

The purpose of this study is to select and analyze words that represent various emotional states towards a spatial environment. Selecting appropriate words for the sensibility evaluation of a spatial environment is a process of constructing sensibility indicators, so that an accurate selection of sensibility words is very important. To collect basic words for this study, words for expressing sensation, emotional states, and sensibility regarding a spatial environment have been collected first via free association and a literature review of previous studies. In the second stage, the selected words are refined. Fifteen evaluators have participated in the first process of refining words, 140 college students participated in the second process, and than the final list of 277 refined words has been selected. During the third stage, 15 specialists were asked to evaluate the appropriateness of sensibility evaluation words, for that 7 point-scale has been applied. Then, 99 words with an average point of 4.55 or more and a standard deviation of 1.55 or lower were selected. After investigating the similarity in the meanings of the selected words, 55 pairs of contrasting words have been selected as a final list of sensibility evaluation words. During this last stage, 307 college students majoring in related fields were asked to evaluate the appropriateness of sensibility evaluation words for a spatial environment, and 7 point-scale was obtained. A factor analysis, cluster analysis, and multidimensional analysis have been conducted on the data obtained from these survey. According to the results of the factor analysis, the eight important factors are obtained from the sensibility evaluation criteria of a spatial environment(form, texture, function, value, comfort, aesthetics, atmosphere, and affinity). The factors obtained from this study can be used in the beginning stage of evaluating the sensibility factors of a spatial environment. In addition, the results of this study can be used as basic data when constructing a list of evaluation indicators to select various complex sensibility words for a space; or as general indicators when evaluating various spatial design factors.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.1
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• pp.83-96
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• 2023

The purpose of this study is to compare and analyze the perception of retro fashion and new-tro fashion using big data. TEXTOM allowed the collection of big data on the words 'retro fashion' and 'new-tro fashion', which was refined afterwards. As for the data collection period, Jan. 1, 2019 to Nov. 30, 2022 was set. A top 50 list of words were extracted from this data based on appearance frequency. The extracted words were processed through Network centrality analysis and CONCOR analysis using Ucinet 6. The results are as follows. 1) In retro fashion, the appearance frequency of 'style' was the highest, followed by 'sensibility', 'color', 'trend', 'fashion', and 'brand'. These words came up with high TF-IDF values. Network centrality analysis discovered that 'color', 'style', 'trend', 'sensibility', and 'design' had high level of connectivity with other words. CONCOR analysis showed a total of four significant groups; trends, styles, looks, and photos. 2) In new-tro fashion, the appearance frequency of 'retro' was the highest, followed by 'trend', 'generation', 'style', 'brand', and 'fashion'. These words also came up with high TF-IDF values. Network centrality analysis found that 'retro', 'trend', 'generation', and 'brand' had high level of connectivity with other words. CONCOR analysis showed a total of four significant groups; style, brand, clothing, and trend. 3) New-tro fashion is included in retro fashion in that it reproduces the styles of the past. However, it is taken completely differently from generation to generation. Unlike the older generations, millennials actively accept newly created clothes and brands based on the past styles. They perceive it as a fashion that reveals their own unique tastes and tastes.

• Language and Information
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• v.15 no.2
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• pp.23-45
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• 2011

This paper proposes a statistical-based linguistic methodology for automatic mapping between large-scale heterogeneous languages resources for NLP applications in general. As a particular case, it treats automatic mapping between two large-scale heterogeneous Korean language resources: Sejong Semantic Classes (SJSC) in the Sejong Electronic Dictionary (SJD) and nouns in KorLex. KorLex is a large-scale Korean WordNet, but it lacks syntactic information. SJD contains refined semantic-syntactic information, with semantic labels depending on SJSC, but the list of its entry words is much smaller than that of KorLex. The goal of our study is to build a rich language resource by integrating useful information within SJD into KorLex. In this paper, we use both linguistic and statistical methods for constructing an automatic mapping methodology. The linguistic aspect of the methodology focuses on the following three linguistic clues: monosemy/polysemy of word forms, instances (example words), and semantically related words. The statistical aspect of the methodology uses the three statistical formulae ${\chi}^2$, Mutual Information and Information Gain to obtain candidate synsets. Compared with the performance of manual mapping, the automatic mapping based on our proposed statistical linguistic methods shows good performance rates in terms of correctness, specifically giving recall 0.838, precision 0.718, and F1 0.774.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.206-207
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• 2018

This study is a series of studies intended to derive the improvement of strength concrete quality using an emulsified refined oil (ERCO). In other words, ERCO is used to analyze the improvement degree of the basic properties of ordinary strength concrete by pre-adding the AE Agent on its products. ERCO was also planned to have a mixing ratio of 0, 0.5 %, and the pre-addtion of AE agent mixed with 0, 1, 2, and 3 % of the concrete's mixed ERCO mass. As a result, as the pre-injection of AE agent was increased, the slump, and air contents tended to be improved microscopically, but there was no significant effect. and Compressive strength tends to increase smart-all as the pre-addtion of AE agent increases in concrete, but it does not have a significant effect.

• Journal of Fashion Business
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• v.12 no.4
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• pp.99-113
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• 2008

The purpose of this study is to compare the social image and the external image of a Korean woman in 1970s and find out that the make-up can provide an important clue to examine the image of a woman in a given period. The research scope covered 10 top news of the daily newspapers and articles of women's magazines. A focus was made to an analysis on words and photos from them. The relationship of each image scale was examined by comparing the linguistic image scale and the visual image scale. The results were as follows : First, a frugal and tidy image. It was the look of our tidy, simple, traditional and classic woman. Second, an image of a cute and pure lady of refined manners. In 1970s, women were supposed to be 'a loving wife', a cultured female image with a faithful role of a 'wise mother' and a lady of refined manners as the best value. Third, a frivolous and decadent image. Double-faced image of a woman which included the women, who had to live as the lady of refined manners during the daytime and seductive woman during the night. Fourth, an image of a contemporary working woman. It was the image as a chic, confident and dignified working woman requested by the society of the times. Namely, it can be understood that women had a make-up of a soft and gorgeous tone as an expression of a will to keep a confident and female aspect in the course of working in the society by the women experiencing 1970s, the turbulent period. Consequently, it is possible to understand that the make-up was utilized as a means to express an ideal beauty of the time pursuant to the historical background or feature.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.111-119
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• 2022

The purpose of this paper is to examine the change in perception of 3D virtual fashion before and after COVID-19 using big data analysis. The data collection period is from January 1, 2017, before the outbreak of COVID-19, to October 30, 2022, after the outbreak. Big data was collected for key words related to 3D virtual fashion extracted from social media such as Naver, Daum, Google, and YouTube using Textom. After the collected words were refined, word cloud, word frequency, connection centrality, network visualization, and CONCOR analysis were performed. As a result of extracting and analyzing 32,461 words with 3D virtual fashion as a keyword, the frequency and centrality of fashion, virtual, and technology appeared the highest, and the frequency of appearance of digital, design, clothing, utilization, and manufacturing was also high. Through this, it was found that 3D virtual fashion is being used throughout the industry along with the development of technology. In particular, the key words that stand out the most after COVID-19 are metaverse and 3D education, which are in high demand in the fashion industry.

• Information Systems Review
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• v.21 no.1
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• pp.91-111
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• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.176-182
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• 2020

In this paper, to analyze the meaning of middle-aged men's economic activities, TEXTOM was used to analyze them. The data collection period is set from 2017 to 2019. Among the collected data, 100 refined words were converted into a matrix in which the degree of social connection was calculated, and the keyword network analysis was performed again with the NetDraw program. According to the study, middle-aged men put more meaning on their current work and family than their future retirement. Also, the related word commonly included in the top five for all three years was 'work'. Related words commonly included in the top 10 were 'old age', 'family', and 'work', and in 2018 and 2019, 'health' was included in the top 10. As a result of this, the middle-aged men living in the modern age are the generation who keep their families through economic activities and are increasingly interested in health and prepare for retirement. Therefore, policy support for stable economic activities is needed to improve the quality of life for middle-aged men. It is necessary to extend the retirement age, expand jobs and provide effective vocational training so that it can handle its role as the head of a family. In addition, measures should be taken to reduce the wage gap between highly skilled and low-skilled workers.

• Journal of the Korean Society of Fashion and Beauty
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• v.2 no.3 s.3
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• pp.44-59
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• 2004

The purpose of this study was to find out proper evaluative words according to fashion image and to identify the correlation between clothing style and hair style related to fashion image. The questionnaire was used to collect data. 326 female aged between 23 to 40 were selected for the subjects of this study. The data were analyzed by frequency, factor analysis, pearsons correlation. The results of this study were as follows. 1. Evaluative words for each fashion image were selected by factor analysis. modern image intellectual, cold, urbane, simple, straight. elegance image : graceful, dignified, refined, decorous, luxurious. romantic image : cute, lovely, girlish, feminine, romantic. natural image : natural, comfortable, gentle, intimate, soft. casual image : energetic, active, free, cheerful, vivid. avant-garde image : experimental, strange, creative, avant-garde, irregular. 2. Correlation between clothing style image and hair style image ; clothing style and hair style was positively correlated. with the same image in case of modern, romantic, casual, elegance and avantgarde but natural image of clothing style was correlated with the natural, elegance, romantic, modern image of hair style. 3. The most suitable hair style for the clothing style according to fashion image : The clothing style of a particular image was matched best with the hair style of the same image.