• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.358-359
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• 2017

실제 영화관에서는 매출을 최대화하기 위해 저마다의 상영관 별 다른 영화 배치 전략을 가지고 있다. 이 영화 배치 전략으로 인해 영화관의 매출이 좌지우지 된다. 여기서 가장 보편적인 기준은 박스오피스이다. 하지만 박스오피스는 과거 영화 상영의 매출액을 모아둔 것으로 개봉되지 않은 영화에 대한 정보는 가지고 있지 않다. 이 개봉되지 않은 영화에 대한 기준, 즉 박스오피스를 얼마나 정확하게 예측 할 수 있는지가 각 영화관의 경쟁력을 결정한다. 본 논문은 개봉 예정인 영화들을 분석하고 이를 통해 박스오피스를 예측는 방법을 제시하고, 실제 박스오피스와 비교, 분석하는 내용을 다룬다.

• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.213-226
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• 2024

This study uses box office data collected by the Korean Film Council (KOFIC) to develop and compare predictive models for cinema attendance and revenue. Data preprocessing removed irrelevant variables and handled missing values separately for categorical and numerical data to ensure consistency. Exploratory data analysis identified key variables, including Seoul audience size, revenue, total number of screens, film genre, rating, and month of release, which revealed a strong correlation between Seoul audience size and revenue with box office performance. Based on this analysis, predictive models were developed using CatBoost and PyCaret AutoML. CatBoost was chosen for its effectiveness in handling categorical variables such as director name, production company, and genre, while PyCaret AutoML was chosen for its ability to automate the modeling process, making it easy for non-experts to compare different models. The performance of the models was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R²), with CatBoost demonstrating superior accuracy. In addition, the SHAP technique was used to interpret the models, identifying Seoul's audience size and revenue as the most significant predictors. This research presents reliable box office prediction models that will improve decision-making in the film industry and support the development of data-driven strategies.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.15-18
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• 2020

특정 영화의 스크린 독과점이 꾸준히 논란이 되고 있다. 본 논문에서는 영화 스크린 분배의 불평등성을 지적하고 이에 대한 개선을 요구할 근거로 머신러닝 기법을 활용한 영화 관람객 예측 모델을 제안한다. 이에 따라 KOBIS, 네이버 영화, 트위터, 구글 트렌드에서 수집한 3,143개의 영화 데이터를 이용하여 랜덤포레스트와 그라디언트 부스팅 기법을 활용한 영화 관람객 예측 모델을 구현하였다. 모델 평가 결과, 그라디언트 부스팅 모델의 RMSE는 600,486, 랜덤포레스트 모델의 RMSE는 518,989로 랜덤포레스트 모델의 예측력이 더 높았다. 예측력이 높았던 랜덤포레스트 모델을 활용, 상영관을 크게 확보하지 못 했던 봉준호 감독의 영화 '옥자'의 상영관 수를 조절하여 관람객 수를 예측, 6,345,011명이라는 결과를 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.35-47
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• 2020

Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73^rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1257-1270
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• 2016

This study deals with the prediction of the total number of movie audiences as a measure for the box office. Prediction is performed by classification techniques of data mining such as decision tree, multilayer perceptron(MLP) neural network model, multinomial logit model, and support vector machine over time such as before movie release, release day, after release one week, and after release two weeks. Predictors used are: online word-of-mouth(OWOM) variables such as the portal movie rating, the number of the portal movie rater, and blog; in addition, other variables include showing the inherent properties of the film (such as nationality, grade, release month, release season, directors, actors, distributors, the number of audiences, and screens). When using 10-fold cross validation technique, the accuracy of the neural network model showed more than 90 % higher predictability before movie release. In addition, it can be seen that the accuracy of the prediction increases by adding estimates of the final OWOM variables as predictors.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.1-14
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• 2021

Recently, crowdfunding platforms have received attention as one of the content investment platforms for the public. This research attempts to explore the influencing factors on the success of movie euqity crowdfunding project. We use 'number of texts', 'number of images', 'star influence power', 'IP-based movie project', 'movie production stage', 'box office prediction', 'investment capital ratio', 'amount of surplus available investment', 'profit calculation method' and 'minimum investment amount' as independent variables. And we examined how these factors affects the achievement rate of movie crowdfunding. As a result of multiple regression analysis, 'movie production stage', 'investment capital ratio', 'amount of surplus available investment' and 'profit calculation method' have a significant effect on the crowdfunding achievement rate. In addition, the results of this research can be used for reference when planning film crowdfunding projects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1087-1095
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• 2016

The study aims to analyze factors of box office utilizing big data. The film industry has been increasing in the scale, but the discussion on analysis and prediction of box-office hit has not secured reliability because of failing in including all relevant data. 13 films have sold 10 million tickets until the present in Korea. The study demonstrated laughs and tears as an main interior factors of box-office hit films which showed more than 10 milling tickets power. First, the study collected terms relevant to laugh and tear. Next, it schematizes how frequently laugh and tear factors could be found along the 5-film-stage (exposition - Rising action - crisis - climax - ending) and revealed box-office hit films by genre. The results of the analysis would contribute to the construction of comprehensive database for the box office predictions on future scenarios.

• Cartoon and Animation Studies
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• s.30
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• pp.245-262
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• 2013

This paper analyzes three feature films that are based on Brothers Grimm's "Snow White": Disney's Snow White and the Seven Dwarfs (1937), Tarsem Singh's Mirror Mirror (2012), and Rupert Sanders' Snow White and the Huntsman (2012). Disney's animation, not the original literature, is the archetype of the later films. Grimm's fairy tail does not include the kiss of Prince Charming that saved Snow White which is, in fact, borrowed from "Sleeping Beauty", nor Snow White's rapport with animals. In Snow White and the Huntsman 's case, the costume of protagonist is similar with Disney's film and some shots are almost identical with Disney's version in terms of composition and angles. Nevertheless, these films show their originality with markedly different visual styles. Mirror Mirror and Snow White and the Huntsman have achieved reasonable success at the box office despite of relatively simple and predictable narratives due to the power of spectacle. While Disney's Snow White displays the model of witch that later becomes prototype of many movies, Mirror Mirror represents the unique magical world, a trompe-l'oell that can only done by director Tarsem, and Snow White and the Huntsman successfully visualizes Freudian concept of 'the uncanny' itself.