• (The) Research of the performance art and culture
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• no.36
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• pp.603-647
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• 2018

During the Japanese colonial era, Chosun Central Young Men's Christian Association Hall, Gyeongseong Public Hall, or Bumingwan was used as multipurpose space where various performances or events were held. Although traditional music performed in each of the places did influence the remaining of traditional music in existence as well as the vitalization of traditional musicians' activity, since these places were mostly for Western music or other genres, no research has been carried out to study the performance of traditional music there in detail. The purpose of this study is to understand the aspects of performing traditional music in multipurpose places mentioned above. Reviewing newspaper materials of the time, this author has examined the aspects of performing traditional music in each of those places in terms of the forms of performances performance programs performers as well as performing groups the aspects of development. First, this researcher considered in what forms traditional music was performed in each of the places mostly. About performance programs, this author divided them into instrumental music, vocal music court music, and folk music. Regarding performers, this study divided them into instrumental music and vocal music professional performers and unprofessional performers male and female master singers and gisaeng and figured out the importance of each of them in different places. Also, concerning the appearance of performers and performing groups, this researcher studied how it was associated with the space, and about the aspects of performance, how it was developed in different periods was examined. In that process, this author has learned that the aspects of performing traditional music in the space were significantly influenced by the characteristics of each space and has clearly and concretely understood performing programs as well as the aspects of performers and groups' activity. With this research as an opportunity, this author expects that research will be actively conducted on the performance of traditional music in each of the places that have not been studied yet and it can contribute to figuring out the development or trends of performing traditional music during the Japanese colonial era.

• Cartoon and Animation Studies
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• s.35
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• pp.107-128
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• 2014

Cultural Content industries based on imagination and creativity are attracting attention and the animation high value-added industry is still spotlighted. Among them, Disney Studios make money through using their professional skills. There are the know-how has developed for a long time, and there are timeless strategy. This study is analyzing by fantasy elements for knowing the messages of Disney animation. Fantasy defined as genre of literature by Todorov and Jackson emphasized its social role and significance. Hume expanded the area of fantasy and Tolkein Jahoriski and Boyer classified the fantasy according to the purpose. Through the analyzing Disney animation , Disney's fantasy show the specific expression depending on the target. Disney is based on the three types of fantasy depending on the growth of the journey. The first area is composed of the main character's growth and satisfy the desire and escapism, The second is the word of antagonist who opposed to reality and break the rules of the dominant ideology. The last area is the utopia provided after hero beat the antagonist. Disney characters give the messages by using the fantasy like the transformation or alter ego. That show the subject of the main character's growth and the supporting characters as the expression of the multiple personality of main. These emphasize the hero's growth and give the fun. Also, in the subject of the otherness, the hero always destroy the evil who broke the rules of reality. In this way, the fantasy offered by Disney give the messages of sacrifice and family from true love. Disney has the support of their target audiences continue to be able to convey ideology.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.