• The Journal of Information Systems
• /
• v.29 no.3
• /
• pp.237-251
• /
• 2020

Purpose The research was studied the hierarchical Hangul emotion index by organizing all the emotions which SNS users are thinking. As a preliminary study by the researcher, the English-based Plutchick (1980)'s emotional standard was reinterpreted in Korean, and a hashtag with implicit meaning on SNS was studied. To build a multidimensional emotion dictionary and classify three-dimensional emotions, an emotion seed was selected for the composition of seven emotion sets, and an emotion word dictionary was constructed by collecting SNS hashtags derived from each emotion seed. We also want to explore the priority of each Hangul emotion index. Design/methodology/approach In the process of transforming the matrix through the vector process of words constituting the sentence, weights were extracted using TF-IDF (Term Frequency Inverse Document Frequency), and the dimension reduction technique of the matrix in the emotion set was NMF (Nonnegative Matrix Factorization) algorithm. The emotional dimension was solved by using the characteristic value of the emotional word. The cosine distance algorithm was used to measure the distance between vectors by measuring the similarity of emotion words in the emotion set. Findings Customer needs analysis is a force to read changes in emotions, and Korean emotion word research is the customer's needs. In addition, the ranking of the emotion words within the emotion set will be a special criterion for reading the depth of the emotion. The sentiment index study of this research believes that by providing companies with effective information for emotional marketing, new business opportunities will be expanded and valued. In addition, if the emotion dictionary is eventually connected to the emotional DNA of the product, it will be possible to define the "emotional DNA", which is a set of emotions that the product should have.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.6
• /
• pp.96-103
• /
• 1999

This paper proposes efficient VLSI architecture for computation of the 1-D discrete wavelet transform (DWT). The proposed VLSI architecture computes the wavelet lowpass and highpass output sequences using the product term anhm, $n,m{\ge}0$, where an and hm denote the imput sequence and the wavelet lowpass filter coefficient, respectively. Whereas the conventional architectures compute the lowpass and highpass output sequences using the product terms anhm and angm, respectively, where gm denotes the wavelet highpass filter coefficient. The proposed architecture is applied to computation of the Daubechies 4-tap wavelet transform using the relationships between the Daubechies wavelet filter coefficients. Performance comparison of various architectures for computation of the 1-D DWT are presented. Note that the proposed architecture does not require extra processing units whereas the conventional architectures need them. Also it is modeled in very high speed integrated circuit hardware description language (VHDL) and simulated to show its functional validity.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.25 no.7
• /
• pp.1258-1269
• /
• 2001

The purpose of this study was to investigate the dimensions of fashion lifestyle, to examine the relationship between fashion lifestyle and internet advertising effect, and to identify the moderating effect of fashion lifestyle on the relationship between advertising types and advertising effects. Using dependent variables as internet advertising effects(i.e., attitude to advertising, attitude to product, attitude to brand), advertising types (i.e., banner, website e-mail types) and fashion lifestyle were used as independent variables. For the study, a sample of 152 apparel consumers participated in this survey research. The survey of design with a questionnaire was employed. Three types of fashion advertisement were included as banner type, website type, and e-mail type. For each type, two samples were included for the study. Questionnaire was developed with the html language and data collection was done through the internet on October 2000. For data analysis, descriptive statistics(i. e., frequency, percent), factor analysis, reliability analysis, linear regression and ANOVA were used. First, fashion lifestyle was classified with the seven dimensions: personality seeking group, planning purchase group, fashion leader group, fashion information seeking group, media preference group, commonness/traditional group, fashion follower group. Second, fashion lifestyle had signification effects on advertising effects. In the group of fashion lifestyle, fashion Information seeking group and planning purchase group were found to influence on the attitude toward advertising, and planning purchase type was influenced to attitude toward brand and attitude toward product. Third, main effects of fashion lifestyle were found to be significant. The correlation and interaction effects of fashion lifestyle and internet advertisement types were not significant.

• Journal of KIISE:Computing Practices and Letters
• /
• v.8 no.6
• /
• pp.746-755
• /
• 2002

EXPRESS is product information description language. It is interpretable by human and software. Product data written in EXPRESS make it possible to exchange between heterogeneous systems. However, the number of software that can use EXPRESS is limited and it is expensive to use the software. XML makes it possible to update and manage data on the Web. Because the Web is easier to use and access than other tools comparatively, data represented by XML need not depend on specific applications or systems and it can be used for exchange of data. Therefore, if we represent EXPRESS-driven data in XML, there will be more active data exchange widely and easily In this work, a method of translation EXPRESS document to XML DTD and XML Schema is proposed. By classification all of EXPRESS syntax element and consideration complex cases caused by this syntax element, a translation rule that represent XML DTD and XML Schema is suggested. Also, a translator which is corresponding to this rule is implemented.

• Journal of the Korean Society for Library and Information Science
• /
• v.49 no.4
• /
• pp.179-201
• /
• 2015

The subject heading is an elaborate access tool for subject browsing and searching in information retrieval environment. The purpose of this study is to suggest the applicable facets to subject headings in Korea. First, the concepts of subject and the definitions of facets were investigated in the literature review. Second, six cases including OCLC's FAST, PRECIS, "Thesaurus construction and use", CC $7^{th}$ edition, BC $2^{nd}$ Edition, and UDC $3^{rd}$ Edition were analyzed to focus on configuration of facets as case studies. Based on the results, twenty-two facets were proposed including Topical, Event, Geography, Chronology, Personal and Corporate Name, Title, Form, Genre, Language, and Person facets as 11 top facets. Also, Topical-Thing/Entity and Topical-Action/Status, Part, Kind, Property, Whole, Material, Patient, Product, By-Product and Agent facets as sub-facets of Topical facet.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.47 no.4
• /
• pp.625-645
• /
• 2023

This study examined newspaper articles from 2012 to the first quarter of 2021 to explore how interest in and response to veganism have evolved in the fashion and beauty industries over the past decade. By analyzing keywords and word correlations, we discovered a steady increase in veganism-related articles in both English- and Korean-language newspapers published in Korea, especially since 2019. Since 2012, consumer interest in vegan fashion materials has grown, with fashion and beauty emerging in 2018 as significant vegan-related keywords. As a result, brands have adopted vegan certification systems and introduced vegan product lines, and new vegan brands have emerged. Since 2020, companies have been promoting environmental, social, and governance (ESG) management practices and working toward eco-management that reflects vegan trends in all areas, such as cruelty-free product/packaging materials, brands, policies, and services. It is also notable that fashion/beauty consumers have been more actively starting to adopt eco-friendly lifestyles and participate in vegan-related movements since that time. Our findings offer important insights into the evolution of veganism in Korea and can help researchers and industry practitioners to develop future business strategies in the vegan fashion and beauty industries.

• Cross-Cultural Studies
• /
• v.45
• /
• pp.219-243
• /
• 2016

The life of Antonin Artaud exactly reproduces a very cruel drama. He lived in constant anguish and suffered from severe mental pain. This research will trace his thoughts in his writings while he was a prisoner of language. Artaud was a poet filled with anxiety about language, things, being, and thought. Whenever he tried to explain the mystery of being by means of mundane language, he experienced psychological agony. His poetic thoughts began to break down, because of his identity loss. Nevertheless, he was destined to grasp the world through language. Artaud had suffered from mental illness during his youth. His mental illness was associated with his difficulty in creating poetry. In this research, the letter, Correspondance avec Jacques $Rivi{\grave{e}}re$, is analyzed. The poet refers to "the collapse of the spirit's core, and the erosion of the fundamental thought that slips away" to convey his linguistic incompetence. Hereafter, he constantly demonstrated anxious mental symptoms. Even though he became mentally deranged, he maintained his consciousness, as is apparent in his writings. Also, his spiritual belief is reflected in his mental uneasiness. While he was traveling through the Tarahumaras area in Mexico, he was obsessed with its primitive belief in the Peyote rituals, and he immersed himself in performing them. His unchristian belief was the product of his mystical personality. Until his last breath, he did not give up writing. Artaud's mental derangement does not mean lunacy, but if one insists in calling it so, that is a metaphor. His derangement comes from his refusal to accept his limitations and from his aspiring to regard his body in the same light as his intellectual perceptions. His intellect could manifest more easily when his mind was elevated to the extreme. Artaud's lunacy is no different from that of a profound philosopher. The lives of poets who suffer from mental derangement are more poetic than the lives of those who do not. Artaud's atypical emotions provide a way of to measure our own limitations, helplessness, and resignation. His scream is nonsegmental but different from that of a mental patient. That difference is why people are interested in his works and wish to delve into his writings.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.8
• /
• pp.181-188
• /
• 2020

In this study, we propose a comparative study to confirm the impact of various word embedding techniques on the performance of sentiment analysis. Sentiment analysis is one of opinion mining techniques to identify and extract subjective information from text using natural language processing and can be used to classify the sentiment of product reviews or comments. Since sentiment can be classified as either positive or negative, it can be considered one of the general classification problems. For sentiment analysis, the text must be converted into a language that can be recognized by a computer. Therefore, text such as a word or document is transformed into a vector in natural language processing called word embedding. Various techniques, such as Bag of Words, TF-IDF, and Word2Vec are used as word embedding techniques. Until now, there have not been many studies on word embedding techniques suitable for emotional analysis. In this study, among various word embedding techniques, Bag of Words, TF-IDF, and Word2Vec are used to compare and analyze the performance of movie review sentiment analysis. The research data set for this study is the IMDB data set, which is widely used in text mining. As a result, it was found that the performance of TF-IDF and Bag of Words was superior to that of Word2Vec and TF-IDF performed better than Bag of Words, but the difference was not very significant.

• Cartoon and Animation Studies
• /
• s.36
• /
• pp.349-374
• /
• 2014

The light is the language. Directors have to describe the scene component effectively his intention to configure the scene as an appropriately. After this act of the character, the layout of the props and scene lights will enter to the scene components. Those things help to audiences can understand narrative of work and emotion that producer want to send. Expressing their emotions especially using the lights by adjusting the colors and contrast makes audience to concentrate on work and understand naturally. This lighting technique clearly appears on early year theaters stage of England and Rembrandt's paintings. Properly dividing and controlling the lights dramatically increases the beauty of the work elements to express a variety of emotions such as worries and fear. Therefore, it can be evolve depending on director's intent of using lights on his work. Lights can increase involvement of human emotion through basic features that cognition of object, visualization of space-time and by artistic method in the product. This study will examine the role and how to use lighting to express the proper sentiment based on the narrative of the work. Making research named "Lighting Research of 3D animated film which applying light features to express emotion" previous study and have to combine emotional vocabulary and emotion-based theory for classifying the emotional language that can be applied on 3D animation. And choosing most emotional scene from 3D animation for analyze how they used lighting to expressing emotions. Directors trying to show up about the light role through light method that matched perfectly with an emotional language. Expecting this research work of directing 3D animations light for expressing emotional feelings will be continue successfully.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.59-83
• /
• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.