• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.263-270
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• 2014

This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

• Journal of Platform Technology
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• v.11 no.3
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• pp.45-55
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• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• The Magazine of the IEIE
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• v.31 no.6
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• pp.40-51
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• 2004

최근 크게 흥행에 성공하고 있는 국산영화들은 국내 영화팬들을 열광케 하고 이제는 해외에서까지 인기를 크게 누리고 있다. 이러한 국산영화의 성공은 심지어 영화에 무관심했던 사람까지도 영화관으로 끌어들이고 관심을 갖게 하고 있으며 영화산업의 발전가능성이 다대함을 증명해 주고 있다.(중략)

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.257-267
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• 2021

This study warns of the dangers of pre-marketing that is too preoccupied with raising market expectations before release at the film market level, and provides practical implications that the lower the level of mismatch of market expectations, the more ideal performance can be achieved. The level of expectation before release and satisfaction after release were measured by each movie rating, and the difference was calculated as the inconsistency of expectations. In raising expectations before the release, the expert rating among the star power, director power, expert rating, and number of screens has a significant influence. It was confirmed that the level of expectation before the release had a positive effect on the rating after the release, but had a negative effect on the level of discordance. In addition, the hypothesis that the higher the expectation level before the release, the greater the inconsistency, and the hypothesis that the higher the expectation inconsistency has a negative effect on the box office level is supported. This provides various implications for marketing carried out before or after the release of a movie to film-related practitioners for products with low involvement or emotional content such as movies.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.56-59
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• 2012

본 연구에서는 최근 활성화되고 있는 게임과 영화의 콜래버레이션 현상에 대해 논의하고 흥행에 관해 분석한다. 이 분석의 결과에 따르면 영화를 원작으로 만들어진 게임은 성공률이 높은 반면 게임을 원작으로 만들어진 영화는 기대만큼의 흥행을 거두기가 어렵다는 것을 알 수 있다. 이런 현상이 발생하는 이유로는 영화자체의 낮은 완성도를 들 수 있고, 이미 게임을 해본 사람들을 주 타킷으로 만들어 졌기 때문에 게임을 하지 않는 대다수의 관객에게 외면을 당했다. 또한 게임과 영화의 본질적인 차이를 고려하지 않고 단순히 게임의 명성에 힘입어 게임 흉내 내기에 그쳤던 것을 알 수 있다. 게임을 원작으로 영화를 제작하려면 먼저 제작자나 감독이 그 게임의 고수가 되어, 게임의 세계관을 이해하고 스토리를 충분히 보존하여 게임을 하는 생생한 느낌을 살리는 연출을 해야 할 것이다.

• Digital Contents
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• no.2 s.153
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• pp.64-69
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• 2006

TV드라마·영화·게임·애니메이션을 막론하고 큰 인기를 얻은 콘텐츠에는 반드시 그 이유가 있기 마련이다. 그런데 그 이유라는 것에서 언제나 빠질 수 없는 요소 중 하나가 바로 스토리텔링이다. 그러나 중요한 것은 흥행에 성공할 것이라고 믿었다가 의외의 실패를 겪은 작품들의 경우에도 바로 흥행작들의 코드나 스토리텔링을 가진 경우들이 대부분이라는 사실이다. 잘된 콘텐츠, 흥행에 성공한 콘텐츠들의 후속 작들 중에는 흥행 코드를 잘 읽어 성공한 경우들만큼이나 실패한 경우들도 있다는 점을 아는 것이 진정한 스토리텔링의 기술을 익히는 길이다.

• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.657-678
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• 2016

This study deals with the effect of online word-of-mouth (OWOM) variables on the box office. From the result of statistical analysis on 276 films with audiences of more than five hundred thousand released in the Korea from 2012 to 2015, it can be seen that the variables showing the size of OWOM (such as the number of the portal movie rater, blog, and news after release) are associated more with the box office than the portal movie rating showing the direction of OWOM as well as variables showing the inherent properties of the film such as grade, nationality, release month, release season, directors, actors, and distributors.

• Journal of Industrial Convergence
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• v.20 no.4
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• pp.31-37
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• 2022

Research on what factors affect the success of the movie market is very important for reducing risks in related industries and developing the movie industry. In this study, in order to find out the degree of correlation of independent variables that affect movie performance, a survey was conducted on film experts using the AHP method and the importance of each measurement factor was evaluated. In addition, we hypothesized that factors derived from big data related to search portals and SNS will affect the success of movies due to the increase in the spread and use of smart phones. And a prediction model that reflects both the expert survey information and big data mentioned above was proposed. In order to check the accuracy of the prediction of the proposed model, it was confirmed that it was improved (10.5%) compared to the existing model as a result of verification with real data.Therefore, it is judged that the proposed model will be helpful in decision-making of film production companies and distributors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.496-499
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• 2019

The study aims to collect detailed movie information from box office of the Korea Film Council and data on Naver's movie ratings to analyze important factors affecting the movie's popularity based on movie audiences and ratings.