• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.995-1004
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• 2007

In this paper, we analyse online word-of-mouth and Korean box-office sales data based on kernel regression method. To do this, we consider the regression model with mixed-data and apply the least square cross-validation method proposed by Li and Racine (2004) to the model. We found the box-office sales can be explained by volume of online word-of-mouth and the characteristics of the movies.

• Korean Management Science Review
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• v.32 no.1
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• pp.133-145
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• 2015

Motion picture industry in Korea has been growing constantly and aroused various kinds of research attention. Particularly, the introduction of official box-office database service brought quantitative studies. However, approaches based on diffusion models have been rarely found with domestic film markets. In addition to the fundamental statistical review on Korea and US film markets, we applied a diffusion model to daily box-office revenue. Unlike conventional preference of Gamma distribution on the film markets, estimation results proved that BMIC can also explain the trend of daily revenue successfully. The comparison with BMIC showed that there is a distinctive difference in diffusion patterns of Korea and US film markets. Generally, word-of-mouth effect appeared more significant in Korea.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.301-309
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• 2013

This paper predicts gross box office revenue for domestic films using the Korean film data from 2008-2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory variables from the data sources available to the public. Some variables must be categorized to conduct more effective analysis and clustering methods are applied to achieve this task. We choose the best model based on performance in the test set and important explanatory variables are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.437-444
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• 2017

Although many studies tried to predict movie revenues in the last decade, the main focus is still to learn an efficient forecast model to fit the box-office revenues. However, the previous works lack the analysis about why the prediction errors occur, and no method is proposed to reduce the errors. In this paper, we consider the prediction error comes from the competition between the movies that are released in the same period. Our purpose is to analyze the competition value for a movie and to predict how much it will be affected by other competitors so as to improve the performance of movie box-office prediction. In order to predict the competition value, firstly, we classify its sign (positive/negative) and compute the probability of positive sign and the probability of negative sign. Secondly, we forecast the competition value by regression under the condition that its sign is positive and negative respectively. And finally, we calculate the expectation of competition value based on the probabilities and values. With the predicted competition, we can adjust the primal predicted box-office. Our experimental results show that predictive competition can help improve the performance of the forecast.

• 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.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.296-310
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• 2017

The present study analyzed the factors affecting the box office performance of movies in the Chinese market in 2010~2014. A total of 499 movies were selected for the final analyses. Based on the previous studies, genre, actor/actress power, director power, sequel, remake, release period, award, online evaluation, distributor power, and production area were chosen as independent variables. Regression analyses showed that most of the independent variables except for distributor power were found to affect box office performance of the movies.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.193-199
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• 2017

This study applied the scenario of the film "Warcraft: The Beginning, 2016" based on the world view and nonlinear story of online games to the basic requirements of a good story of five items. Based on this, it was found that the storyline of the film "Warcraft: The Beginning, 2016" was faithful to the game series with the same title, but weak for a film. This film did not satisfy any of the five requirements of a good scenario used for Hollywood production. Even before this film, many hit games were made into films, but box office hits are hard to find among them. As the game World of Warcraft was very famous worldwide, when it was made into a film, the film was expected to be a box office hit. However, it failed miserably at the box office. This is a result of failing to recognize that the characteristics of games that an individual leads the story are different from those of films that no interaction or choice can be made. It is necessary to understand that although games and films are the same in that they have narrative structures and visual stimulation graphics, they are totally different in the way they immerse people.

• Cartoon and Animation Studies
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• s.16
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• pp.21-32
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• 2009

This study provides an empirical analysis of box office performance for animation movies released in Korea between 2003 and 2008. Two dependent variables are both the number of audiences in the whole country and the number of audiences in Seoul. Such independent variables are employed : power of distributors, the number of screens, release time, sequel/remake, awards, film ratings, nationality, online reviews, and critics' reviews. For the total number of audiences in the whole country, significant variables are the number of screens, the power of USA distributors, Summer release, and online reviews. Since there is no analysis for box office performance for animation movies released in Korean theaters, this study will be considered to be meaningful.