• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.69-94
• /
• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of the Korean Society of Costume
• /
• v.32
• /
• pp.313-336
• /
• 1997

Nineteen stimulus photograghs varied in hue and color scheme of one clothing style of Korean dress worn by a female were used to investigate the effect of color color scheme and structure on impression formation for Korean dress. Subjects were 77 male and 86 female undergraduate and graduate students. The stimuli c9onsisted of two sets(cool and warm) of four similar color schemes two sets (cool and warm in Chima color) of five contrasting color schemes and one extra stimulus triad 3 hue base. Structures were de-fined by color schemes of Kit.Korum toward the color schemes of Jokori and Chima. Stimu-lus photogragh selected from Korean dress fashion magazines was managed and varied in hues and color schemes to Kit Korum Jokori and Chima according to Korean Standard Color through scanning and Adobe photoshop 3.0 program and then pictured through slide printer(HR-6000). Each subject assessed 19 stimulus color photographs with incorporated 7 point semantic differential response scale. The data were analyzed by frequency mean factor analysis t-test ANOVA and Scheffe test. Results indicate impression ofrmations are af-fected by clothing cues. 1) Four factors emerged to account for dimensional structure of impressions of female features on Korean dress. These four factors were titled as(1) preference.evaluation (2) individuality.attention (3) youth and (4) friendshio. The preference.evaluation factor was the largest including eleven adjectives and accounting for 29.62% of the variances. 2) Almost every clothing cue(color, color scheme, structure) had some effects on im-pressions formed But the color of Chima did not form the effects on impression of prefer-ence.evaluation factor. The effect of related color scheme was the most influential clothing cue on impressions of preference.evalation factor and friendship factor whereas the ef-fect of contrasting color scheme was the most influential clothing cue on impressions of indi-viduality.attention factor and youth factor. The effect of cool color of Chima was the most influential clothing cue on impression of indi-viduality.attention factor whereas the effect of warm color of Chima was the most influen-tial clothing cue on impressions of youth factor and friendship factor. The effect of Jokori/Chima.Kit.Korum structure was the most influential clothing cue on impressions of pref-erence.evaluation factor and youth factor whereas the effect of Kit.Korum/Jokori.Chima structure was the most influential clothing cue on impressions of individuality.attention factor and friendship factor. 3) The interaction effects were appeared among clothing cues. Significant interaction effects between color schemes(similar and contrasting) and colors of Chima(cool and warm were appeared on impressions of prefer-ence.evaluation factor imdividuality.atten-tion factor and friendship factor, Significant interaction effects between color schemes (similar and contrasting) and structures (Jokori.Chima.Kit.Korum; Jokori.Kit.Koru-m/Chima;Jokori/Chima.Kit.Korum;Kit.Korum/Jokori.Chima) were appeared on impressions of preference.evaluation factor youth factor and friendship factor. Signifi-cant interaction effects between colors(cool and warm) and structures were appeared on impressions of individuality.attention factor youth factor and friendship factor. Sighifi-cant interaction effects between colors(cool and warm) and structures were appeared on impressions of individuality.attention factor youth factor and friendship factor. Significant interaction effects among clothing cues(color color schemes and structures) were appeared on all impression factors. The friendship factor was the most friquently affected impression factor by interaction effects among clothing cues. In summary the clothing was used as nonverbal cues in the effect on impression for-mation of female dressed in Korean dress. it concluded that color schemes worked as cen-tral traits and colors of Chima and structures worked as peripheral traits in the formation of impression of the female clothed in Korean dress. hence organizing our impressions with respect to the parts of the Korean dress in re-lation to the whole holistic perceptual pro-cess Gestalt approach was used and supported.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.47 no.4
• /
• pp.33-49
• /
• 2019

The purpose of this study was to investigate the changes in the experiences of visitors of Yeonnam-dong during the period covering the development of a linear park, the Gyeongui Line Park. This study used a text mining technique to analyze Naver Blog postings of those who visited Yeonnam-dong from June 2013 to May 2017, divided into four periods -from June 2013 to May 2014, from June 2014 to May 2015, from June 2015 to May 2016 and from June 2016 to May 2017. The keywords used were 'Yeonnam-dong', 'Gyeongui Line' and 'Yeontral Park' and the data was further refined and resampled. A semantic network analysis was conducted on the basis of the co-occurrences of words. The results of the study were as follows. During the entire period, the main experience of visitors to Yeonnam-dong was 'food culture' consistently, but the activities related to 'market', 'browsing', and 'buy' increased. Also, activities such as 'walk', 'play' and 'rest' in the park newly appeared after the construction of the park. Moreover, more diverse opinions about the Yeonnam-dong were expressed on the blog, and Yeonnam-dong began to be recognized as a place where a variety of activities can be enjoyed. Lastly, when the visitors wrote about the theme 'food culture', the scope of the keywords expanded from simple ones, such as 'eat', 'photograph' and 'chatting' to 'market', 'browsing', and 'walk'. The sub-themes that appeared with the park also expanded to various topics with the emergence of the Gyeongui Line Book Street. This study analyzed the change of experiences of visitors objectively with text mining, a quantitative methodology. Due to the nature of text mining, however, the subjective opinions inevitably have been involved in the process of refining. Also, further research is required to assess the direct relationship between these changes and park construction.