• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.19-30
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• 2018

The main purposes of this study are to analyze the contents and text network of online tourism information. For this purpose, Jeju Island, one of the representative tourist destinations in South Korea is selected as a study site. And this study collects the contents of both JeJu official tourism website and Sina Weibo's personal blogs which is one of the most popular Social Network Systems in China. In addition, this study analyzes this online text information using ROST Content Mining System, one of the Chinese big data mining systems. The results of the content analysis show that the formal website of Jeju includes the nouns related to natural, geographical and physical resources, verbs related to existence of resources, and adjectives related to the beauty, cleanness and convenience of resources mainly. Meanwhile, personal blogs include the nouns of Korean-wave, food, local products, other destinations and shopping, verbs related to activity and feeling in Jeju, and adjectives related to their experiences and feeling mainly. Finally, the results of text network show that there are some strong centrality and network of online tourism information at formal website, but there are weak relationships in personal blogs. The results of this study may be able to contribute to the development of demand-based marketing strategies of tourists destination.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.89-102
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• 2024

This study aims to examine the characteristics of the user experience of New Town neighborhood parks and explore issues that diversify the experience of the parks. In order to quantitatively analyze a large amount of park visitors' experiences, text-based Naver blog reviews were collected and analyzed. Among the Phase 1 and 2 New Towns, the parks with the highest user experience postings were selected for each city as the target of analysis. Blog text data was collected from May 20, 2003, to May 31, 2022, and analysis was conducted targeting Ilsan Lake Park, Bundang Yuldong Park, Gwanggyo Lake Park, and Dongtan Lake Park. The findings revealed that all four parks were used for everyday relaxation and recreation. Second, the analysis underscores park's diverse user groups. Third, the programs for parks nearby were also related to park usage. Fourth, the words within the top 20 rankings represented distinctive park elements or content/programs specific to each park. Lastly, the results of the network analysis delineated four overarching types of park users and the networks of four park user types appeared differently depending on the park. This study provides two implications. First, in addition to the naturalistic characteristics, the differentiation of each park's unique facilities and programs greatly improves public awareness and enriches the individual park experience. Second, if analysis of the context surrounding the park based on spatial information is performed in addition to text analysis, the accuracy of interpretation of text data analysis results could be improved. The results of this study can be used in the planning and designing of parks and greenspaces in the Phase 3 New Towns currently in progress.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.75-86
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• 2016

This study examines the social issues between the central and local governments related with the underground space after happenings of sinkholes in Jamsil area in July, 2014. In this study, we consider the keyword network of the social network analysis as a research methodology. The social issues regarding the underground space have been dealt with through the analysis of the centrality and group density to know the attributes of the network. The results show that the government has been steadily helpful to the local governments for establishing the socialized law for the underground space. This research suggests that the laws and technologies as to the underground space issues cooperate each other in the future. It also shows that the government should enact the policies and the national plans for the development of the underground.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.1
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• pp.191-207
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• 2017

This paper employs entitymetrics analysis on the research works of Dong-ju Yun. He was a Korean poet who was studied by many researchers on his works, religion and life. We collected 1,076 papers about Dong-ju Yun and conducted various approaches including co-author citation analysis, topic modeling analysis to identify the topic trend in the study of Dong-ju Yun. Also we extracted entities like person's name and literature's title from abstract to examine the relationship among them. The result of this paper enables us to objectively identify the topic trend and infer implicit relationships between key concept associated with Dong-ju Yun based on text data. Moreover, we observed sub-research topics such as life, poem, aesthetic existence, comparative literature, literary translation, and religious beliefs. This paper shows how entitymetrics can be utilized to study intellectual structures in the humanities.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.127-132
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• 2016

Several methods have been proposed to apply artificial neural networks to text compression in the past. However, the networks and targets are both limited to the small size due to hardware capability in the past. Modern GPUs have much better calculation capability than CPUs in an order of magnitude now, even though CPUs have become faster. It becomes possible now to train greater and complex neural networks in a shorter time. This paper proposed a method to transform the distribution of original data with a probabilistic neural predictor. Experiments were performed on a feedforward neural network and a recurrent neural network with gated-recurrent units. The recurrent neural network model outperformed feedforward network in compression rate and prediction accuracy.

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.490-505
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• 2017

The purpose of this study was to analyze the research trend of transformational leadership published in domestic journals in the last 10 years and to find some implications for future research. For this purpose, 337 research papers on transformational leadership from 2007 to 2016 were reviewed. This study used descriptive statistics by frequency and percentage and a network text analysis method. The findings of the study are as follows. First, the annual average number of papers published was 33. Second, 'human resource management research' was the most common topic. Third, most of the research subjects were business employees. Fourth, the research method trend analysis showed that the highest proportion was in the quantitative research. Fifth, 'transactional leadership' showed the highest frequency as a result of analyzing the keywords presented in the abstract of the paper. Sixth, as a result of analyzing the network texts, those having the trend of being analyzed with a close connections were 'transactional leadership', which had the highest connection to transformational leadership, showing the closest relationship with 'role satisfaction'.

• Journal of Korean Library and Information Science Society
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• v.36 no.2
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• pp.327-348
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• 2005

The purpose of this study is to emphasize the importance of index definition and to prepare the basis of optimal index in bibliographic retrieval system. For the purpose, this research studied a index extraction theory on index tag definition and index normalization from the bibliographic marc database and analyzed a retrieval utilization rate of extracted index. In this experiment, we divided index between text-type and code-type about the generated 29,219,853 indexes from 2,200,488 bibliographic records and analyzed utilization rate by the comparison of index-type and index term of web logs. According to the result, the text-type indexes such as title, author, publication, subject are showed high utilization rate while the code-type indexes were showed low utilization rate. So this study suggests that the unused index is removed from index definition to optimize index.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.575-583
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• 2002

JBIG2 is an International Standard fur compression of Bi-level images and documents. JBIG2 supports three encoding modes for high compression according to region features of documents. One of which is generic region coding for bitmap coding. The basic bitmap coder is either MMR or arithmetic coding. Pattern matching coding method is used for text region, and halftone pattern coding is used for halftone region. In this paper, a document is segmented into line-art, halftone and text region for JBIG2 encoding and JBIG2 CODEC is implemented. For efficient region segmentation of documents, region segmentation method using wavelet coefficient is applied with existing boundary extraction technique. In case of facsimile test image(IEEE-167a), there is improvement in compression ratio of about 2% and enhancement of subjective quality. Also, we propose arbitrary shape halftone region coding, which improves subjective quality in talc neighboring text of halftone region.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1386-1399
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• 2004

This paper presents correlation-based automatic image captioning. Given a training set of annotated images, we want to discover correlations between visual features and textual features, so that we can automatically generate descriptive textual features for a new unseen image. We develop models with multiple design alternatives such as 1) adaptively clustering visual features, 2) weighting visual features and textual features, and 3) reducing dimensionality for noise sup-Pression. We experiment thoroughly on 10 data sets of various content styles from the Corel image database, about 680MB. The major contributions of this work are: (a) we show that careful weighting visual and textual features, as well as clustering visual features adaptively leads to consistent performance improvements, and (b) our proposed methods achieve a relative improvement of up to 45% on annotation accuracy over the state-of-the-art, EM approach.