• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.103-116
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• 2020

This paper starts with the recognition of the problem of the need for a link between text-centered acting and body-centered acting. This study is focused on Brecht's theory of acting to overcome loss of presence by repetition which have been discussed many times by not only actors, but also acting educators. Brecht's acting theory has already been mentioned by many researchers as an alternative to conventional actor training. However, not many studies have been conducted on practical applicable methods. The purpose of this study is to provide the basis for the actual practice of Brecht acting and possibility that his acting theory can serve as a link between text and body-centered acting theory. As a research method, we first conduct theoretical considerations on the concepts and limitations of text-centered representational acting and body-centered post-drama acting. Then distinguish between text and body-centered acting tools among Brecht's epic theatre, to summarize the terms and concepts he uses and to identify the existing effects he reaches while acting. Finally, this paper proposes an teaching model that transforms and develops Brecht's acting theory through the writer's teaching experience. However, there are limitations in generalizing its effectiveness because this study is based on the writer's experience. We hope that further research will help the diversity of acting education by developing in-depth insights on Brecht acting theory and various models of acting teaching methods.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.253-274
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• 2020

In this study, text mining and main path analysis (MPA) were applied to understand the origins and development paths of research areas that make up the mainstream of Korean studies. To this end, a quantitative analysis was attempted based on digital texts rather than the traditional humanities research methodology, and the main paths of Korean studies were extracted by collecting documents related to Korean studies including citation information using a citation database, and establishing a direct citation network. As a result of the main path analysis, two main path clusters (Korean ancient agricultural culture (history, culture, archeology) and Korean acquisition of English (linguistics)) were found in the key-route search for the Humanities field of Korean studies. In the field of Korean Studies Humanities and Social Sciences, four main path clusters were discovered: (1) Korea regional/spatial development, (2) Korean economic development (Economic aid/Soft power), (3) Korean industry (Political economics), and (4) population of Korea (Sex selection) & North Korean economy (Poverty, South-South cooperation).

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.282-290
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• 2018

This study tried to suggest the development of a writing education model for Christian Universities based on both Hobbs' media education model and Vanhoozer's media text analysis model. This model consists of 6 steps- Christian worldview establishment, access, analysis and evaluation, creation, reflection and social action. This was developed in order to be applied for the class "reading and writing" of liberal arts. Also, this is an appropriate model for media text writing education aiming at an alternative creation activity through a critical comprehension of the complex texts consisting of sounds, images, letters and so on. Furthermore, this is designed to train the capable persons having intelligence, character, and spirituality balanced, whom the education of Chrisitian universities aims at. Finally, this model pursues the student-friendly and amalgamative text writing appropriate for a new era and has an advantage to raise the power of various forms of letter writing which all the universities should stress as well.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.1-12
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• 2020

The purpose of this study is to explore the trend of blockchain technology through analysis of patents and news articles using text mining, and to suggest the blockchain policy agenda by grasping social interests. For this purpose, 327 blockchain-related patent abstracts in Korea and 5,941 full-text online news articles were collected and preprocessed. 12 patent topics and 19 news topics were extracted with latent dirichlet allocation topic modeling. Analysis of patents showed that topics related to authentication and transaction accounted were largely predominant. Analysis of news articles showed that social interests are mainly concerned with cryptocurrency. Policy agendas were then derived for blockchain development. This study demonstrates the efficient and objective use of an automated technique for the analysis of large text documents. Additionally, specific policy agendas are proposed in this study which can inform future policy-making processes.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.17-27
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• 2020

News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.68-74
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• 2019

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.1-15
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• 2018

The purpose of this study is to analyze bibliographic information of Busan and other cities' reports for urban development initiative and identify the strategic fields for future smart city plan. Text mining method is used in this study to extract keywords and identify the characteristics and patterns of information in urban development reports. As a result, in earlier stage, Busan city focused on service creation for industrial development but there are lack of discussions on the linkage of information systems with ICT technology. However, recent urban planning in Busan contained various contents related to integrated connections of infrastructure, ICT system, and operation management of city in the specific fields of traffic, tourism, welfare, port/logistics, culture/MICE. This results of study is expected to provide policy implications for planning the future urban initiatives of smart city development.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.891-897
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• 2022

In big data analysis, raw text data mostly exists in various unstructured data forms, so it becomes a structured data form that can be analyzed only after undergoing heuristic pre-processing and computer post-processing cleansing. Therefore, in this study, unnecessary elements are purified through pre-processing of the collected raw data in order to apply the wordcloud of R program, which is one of the text data analysis techniques, and stopwords are removed in the post-processing process. Then, a case study of wordcloud analysis was conducted, which calculates the frequency of occurrence of words and expresses words with high frequency as key issues. In this study, to improve the problems of the "nested stopword source code" method, which is the existing stopword processing method, using the word cloud technique of R, we propose the use of "general stopword corpus" and "user-defined stopword corpus" and conduct case analysis. The advantages and disadvantages of the proposed "unstructured data cleansing process model" are comparatively verified and presented, and the practical application of word cloud visualization analysis using the "proposed external corpus cleansing technique" is presented.

• Smart Media Journal
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• v.12 no.5
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• pp.17-27
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• 2023

Recently, automatic text summarization, which automatically summarizes only meaningful information for users, is being studied steadily. Especially, research on text summarization using Transformer, an artificial neural network model, has been mainly conducted. Among various studies, the GSG method, which trains a model through sentence-by-sentence masking, has received the most attention. However, the traditional GSG has limitations in selecting a sentence to be masked based on the degree of overlap of tokens, not the meaning of a sentence. Therefore, in this study, in order to improve the quality of text summarization, we propose SbGSG (Semantic-based GSG) methodology that selects sentences to be masked by GSG considering the meaning of sentences. As a result of conducting an experiment using 370,000 news articles and 21,600 summaries and reports, it was confirmed that the proposed methodology, SbGSG, showed superior performance compared to the traditional GSG in terms of ROUGE and BERT Score.

• Archives of design research
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• v.19 no.5 s.67
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• pp.233-242
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• 2006

The purpose of this study is to investigate communication efficacy of advertisement text through audience responses. This study approached qualitatively to KT&G TV commercials. The research proceeded as follows: First, as background theory, studies on audience focusing advertisement criticism and the interpretation of advertising texts were reviewed. Secondly, the characteristics of the audiences of the advertisements, analyzed through in-depth individual interviews as well as group interviews, were incorporated into a broad theme and then divided into different dimensions. Finally, the audiences' decoding code and critic reponses in reading ambiguous advertising texts, and the interrelationship between strategic ambiguity were discussed under a unified model. The major findings of this study are as follows: In interpreting the ambiguous advertising texts, the audiences use various decoding codes such as language, visuals, technology and rhetoric, and various critic responses such as linguistic, macroscopic, schematic, non-verbal and socio-cultural factors, in quite comprehensive manner. Also, it was shown that audiences make use of different decoding strategies in terms of their recognition, reliability, emotional attitude, and behavior. It can therefore be concluded that the strategic ambiguity has its limit in explaining its effectiveness in the entire dimensions of recognition, emotional attitude, and behavior, in the sense that the strategic ambiguity is most effective in recognition while it invokes more negativity in the behavioral dimension. Finally, this empirical study, focusing on qualitative analyses, may have its limit as well; however, deeper statistic-qualitative studies in the future could compensate for it.