• Journal of KIISE
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• v.44 no.11
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• pp.1236-1243
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• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Journal of the Korean Society of Costume
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• v.54 no.7
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• pp.41-54
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• 2004

This study purposed to apply function form content, the three concepts that have been discussed by many philosophers since ancient times, to fashion design. Specific research goals are : first, to define fashion design based on the three concepts : and second, to examine how each of the three concepts function-oriented, form-oriented and content-oriented design have been expressed in women's fashion in the $20^{th} century. For these purposes. the author considered Morris' semiotics, which is the theoretical background of the three concepts, reviewed previous researches in design area, and applied the findings to fashion design. According to the result of applying the threefold structure of semiotics. which is the theoretical background of the three concepts, the pragmatic dimension of fashion design comprehends all functional rules related to the use of dress such as body motion and protection, health and safety. air flow and durability, and its syntactic dimension comprehend all the formal elements of visual design such as the structure, shape, line, color and material of dress. The semantic dimension of fashion design includes the symbolic meanings of dress expressed by emotion, sentiment and images. The three dimensions exist interdependently with one another. According to the result of considering the characteristics of the three concepts in the scope of women's fashion in the $20^{th} century. function-oriented design is characterized by practicality and simplicity, and has been expressed as the fashion of functionalism in the 1920s, that of minimalism in the 1960s, and the basic style from 1970s to 1980s, 1990s and the present. Form-oriented design has pursued aestheticism, putting stress upon form, and has been expressed with organic shapes imitating patterns found in nature in the 1950s and with optical art fashion in the 1960s. Content-oriented design attaches importance to transmission of delicate meanings related to the mental world of human beings, and is represented with symbolic forms. Such a characteristic has been expressed in fashion in the early 20th century influenced by surrealism and, with various types of design breaking established forms as well as metaphors and humors that characterize design in the late 20th century.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.6
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• pp.1017-1026
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• 1995

The purpose of the study was to 1) extend the cognitive categorization theory in an attempt to explain the effect of garment formality, Yin-Yang, and body type of children on impression formation, and 2) to understand teacher's attitudes toward children's school outfits. The experimental design was a $2^3$_full factorial design by 3 independent variables. The stimuli consisted of 8 color photographs and the semantic differential response scale was used to analyze the responses of 267 teachers of elementary school. The data were analyzed by factor analysis, ANOVA, Duncan' test and content analysis. Four factors emerged to account for dimensions of first impressions. These were sociability, potency, dynamics, and cooperation. Garment formality effected on impression of cooperation dimension. Garment Yin-Yang and children's body type effected on impression of social and dynamics dimensions.

• Journal of the Korean Home Economics Association
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• v.40 no.1
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• pp.135-146
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• 2002

The Purpose of this study was to identify the sensibility elements and the evaluative dimensions of internet fashion shopping mall to supply optimal experience to the customer. First, association words for internet fashion shopping mall by open-ended method and sensibility-expression-adjective feeling as navigating personally 57 shopping mall dealing with fashion products were collected. Collected adjectives were ranked after making index by frequency and diversity. Then, correlation analysis was executed to extract independent adjective and their opposite words. Semantic differential scale was made for internet-fashion-shopping-mall-evaluation. After preliminary investigation with this scale, factor analysis was implemented. 12 sensibility evaluation words were extracted. Then, 200 subjects evaluated satisfaction degree for 8 selected shopping mall. To explain the hierarchy of internet fashion shopping mall, cluster analysis was applied. The understanding of sensibility element and evaluative dimensions of internet fashion shopping mall can be utilized efficiently as basic materials when marketer plans internet shopping mall design and makes marketing strategy.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.9
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• pp.1331-1341
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• 2002

The purpose of the study is to clarify orange image in the modem fashion. So kinds of costume sample being visual power in orange have been selected from photographs in fashion magazines and divided into the tones : mist(Vp, Lgr, L), bright(p, B), vivid(S, V, Dp). The study was measured by using 27 semantic differential hi-polar scales. The subjects were 50 female students majoring in clothing and textiles, The data was analyzed using the statistical SPSS package. The data were collected using self-administred questionnaires and analyzed by MDS, Cluster Analysis, ANOVA Sheff test and Regression analysis. The major findings of this research were as follows. 1. Evaluaion dimension of orange was classified as Feminine-Mannish, Lively-Mist.2. There were significant difference in visual evaluation of tones.3. The image effect on Preference, Buying needs, Pleasant and Riches was consist of complicated sensibility.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.703-708
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• 2009

The present paper describes the application of an improved impression transfer vector method (Sakurai et al., 2007) to transform the three basic dimensions (Evaluation, Activity, and Potency) of higher-order impression. First, a set of shapes and surface textures of faces was represented by multi-dimensional vectors. Second, the variation among faces was coded in reduced parameters derived by applying principal component analysis. Third, a facial attribute along a given impression dimension was analyzed to select discriminative parameters from among principal components with higher sensitivity to impressions, and obtain an impression transfer vector. Finally, the parametric coordinates were changed by adding or subtracting the impression transfer vector and the image was manipulated so that its facial appearance clearly exhibits the transformed impression. A psychological rating experiment confirmed that the impression transfer vector modulated three dimensions of higher-order impression. We discussed the versatility of the impression transfer vector method.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.417-421
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• 2004

최근 들어 사진측량은 광센서 및 컴퓨터기술 발전과 같은 관련분야의 발전과 자료처리과정의 자동화 및 사용자의 편의성에 대한 요구로 수치사진측량(Digital Photogrammetry)이라는 새로운 학문분야로 발전하고 있다. 수치사진측 량은 지형지물 및 자연환경에 대한 기하(geometric), 복사(radiometric) 및 의미적(semantic) 정보를 수치영상으로부터 획득하는 기술로 정의되고 있다. 그 적용분야 역시 크게 증가하여 사회 전반에 응용되고 있으며 앞으로 GPS(Global Positioning System), GIS(Geographic Information System) 및 RS(Remote Sensing)와 연계되어 발전 및 응용의 잠재력이 클 것으로 예상된다. 따라서 본 연구에서는 수치사진측량의 발전과정 및 정확한 지형자료의 해석을 중심으로 수치사진측량에 의한 영상 정보를 보다 쉽고, 정확하게 해석하여 정보 취득 관한 적용 가능성을 제시하고, 도시지역의 지상 D/B자료와 지하시설물 D/B를 하나의 3차원 시각화로 나타냄으로써 도시의 모든 시설물을 통합하여 보다 효과적인 유지ㆍ관리, 재해방지, 합리적 의사결정에 기여하고자 한다.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.195-210
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• 2007

Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.