• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.316-318
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• 2021

영화 수익에 있어 영화의 흥행 여부는 중요한 영향을 끼친다. 영화 흥행 요인은 영화 산업의 규모가 커지면서 많은 제작사들 및 투자자들이 고려해야 하는 사항이 되었다. 따라서 영화의 흥행을 예측하기 위한 많은 모델이 연구되었다. 본 연구의 목적은 선행연구에서 흥행에 유의미한 영향을 끼친다고 밝혀진 스크린 수, 감독명, 제작사명 등의 내재적인 속성과 더불어 온라인 구전 변수를 사용하여 영화 흥행 예측 모델을 만드는 것이다. 이때 기사 수, 블로그 수와 같이 온라인 구전의 크기를 나타내는 변수들을 사용하는 대신 개봉 후 첫 주간의 관람객 리뷰를 텍스트마이닝을 이용하여 전체 리뷰 중 긍정 리뷰의 비율에 따라 점수를 매긴 후 독립변수로 사용한다. 그 후, 데이터 마이닝 기법을 활용하여 만든 모델에 앞서 언급한 독립변수를 입력 값으로 사용하여 영화의 흥행을 예측한다. 최종적으로 의사결정트리와 로지스틱회귀를 수행한 결과 영화 흥행에 영향을 주는 독립변수를 찾고 모델의 성능을 평가하였다. 로지스틱회귀의 결과 관객 수, 평점이 영화의 흥행에 특히 유의한 영향을 끼치는 변수로 선정되었고 리뷰 역시 유의한 변수로 선정되었다. 이때 만들어진 모델은 약 90%의 높은 수준의 정확도를 보여주었다. 의사결정트리의 결과 관객 수가 가장 중요한 변수로 선정되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.556-558
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• 2016

영화 흥행 실패의 리스크를 줄이기 위해 객관적인 흥행 예측 지표가 요구된다. 본 논문에서는 영화 스크립트의 텍스트를 분석하여 흥행성과를 예측하는 기법을 제안한다. 객관적인 흥행 예측 지표는 누적 관객 수와 누적 매출액으로 설정하였다. 실험은 2010년 1월 1일부터 2016년 8월까지 개봉한 영화중에서 누적 관객 수와 누적 매출액을 기준으로 상위 50위까지의 영화 스크립트를 분석하여 진행했다. 실험을 통해 영화 제작에 앞서 스크립트 분석만을 활용한 영화 흥행성과 예측이 가능함을 보였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.167-173
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• 2017

With increased interest in the fourth industrial revolution represented by artificial intelligence, it has been very active to utilize bigdata and machine learning techniques in almost areas of society. Also, such activities have been realized by development of forecasting systems in various applications. Especially in the movie industry, there have been numerous attempts to predict whether they would be success or not. In the past, most of studies considered only the static factors in the process of prediction, but recently, several efforts are tried to utilize realtime social bigdata produced in SNS. In this paper, we propose the prediction technique utilizing various feedback information such as news articles, blogs and reviews as well as static factors of movies. Additionally, we also experimentally evaluate whether the proposed technique could precisely forecast their revenue targeting on the relatively successful movies.

• Archives of design research
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• v.12 no.4
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• pp.23-32
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• 1999

The 'Hard Matte' was the method being wide-screen to the directors wanted more vivid and made real films to the audience at the beginning of cinematography. The hard matte have changed as unique device in films as opening and closing credit titles making impressions which being captured strongly to the people at the time the wide-screen has developed. In HDTV has invented in NHK the hard matte is called 'letter box style and on music video channel we can see the style as easily. That kind of hard matte images on music video have effected to commercial films are seen recently. But some of commercial films have problems like only expending horizontal way. The hard matte is one of important ways that could regulate the art contents with art form. Korean film image makers as directors and production designers must know the role and function of hard matter has arranged reasonable in foreign films.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.289-296
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• 2015

The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.242-247
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• 2009

Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.9-17
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• 2021

Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters.

• 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.

• Journal of the Korea Computer Graphics Society
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• v.18 no.3
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• pp.17-27
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• 2012

In this paper, we describe a case study of the film 'Sector 7' which was produced by technologies applied fluid simulation. For the CG scenes in the movie which include highly detailed fluid motions, we used smoothed particle hydrodynamics(SPH) technique to express subtle movements of seawater from a crashed huge tank, and used hybrid simulation method of particles and levelsets to describe bursting water from a submarine's broken canopy. We also used detonation shock dynamics(DSD) technique for detailed flame simulations to produce a burning monster, the film"s main character. At this point, the divergence-free vortex particle method was applied to conserve the incompressible property of fluids. In addition, we used an upsampling method to achieve more efficient video production. Consequently, we could produce the high-quality visual effects by using the domestic technologies.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.91-97
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• 2019

The movie Lolita is directed by the famous director Stanley Kubrick in the United Kingdom in 1962. Due to the movie theme, it produced a great social dispute at that year. Lolita was Stanley Kubrick's first independently produced film. It tells the story of the abnormal love between Professor Humbert from Europe and his stepdaughter Lolita. Throughout the film, it shocked people by feeling honourable and aesthetic although the theme is about commit incest. The director also completely shown the social reality of the United States in a black humor irony method. By the meantime, the lush tragic feature of the film also strongly infects the audience. Current studies on the movie Lolita mainly focus on the following aspects: firstly, the black humor techniques of director Kubrick's movies, secondly, the parody techniques of the movie, and thirdly, the differences between the original novel Lolita and the adapted movie. In the American society where consumerism constitutes the mainstream, instead of sticking to traditional moral concepts, people pursue material enjoyment. Based on the consumerism theory, this paper analyzes the social reality revealed by the movie Lolita, presents such typical characteristics of the consumerist society as hedonism and broken family relations, and explains how the consumerist society leads to Lolita's tragic life.