• 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.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.221-229
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• 2020

This study aims to find out predictors of a movie's eventual box office, focusing on a movie's box office rank on an opening day. For an empirical analysis, I use data of 794 commercial movies released in the Korean theatrical exhibition market during the recent five years(2013~2017). The regression analysis shows that being box office number one on an opening day has statistically significant impact on a movie's final box office. A movie's quality, measured by audience's review, does not give significant impact on top1 movie's final success. Indexes such as increase of showing number on Sunday are strongly related to a movie's economic success. The additional analysis on box office number one movie on an opening day, totally 158 movies, finds that box office growth rate on a second week is strongly related to a movie's final success. Lastly, correlation coefficient of a movie's opening day box office and final box office does not show consistent growth when the coefficient is compared yearly. This study might have meaning in that it proposes new box office predictors and shows the relation of the indexes and a movie's final performance empirically.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.405-412
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• 2018

When a film company decides whether to invest or not in a scenario is the appropriate time to predict box office success. In response to market demands, AI based scenario analysis service has been launched, yet the algorithm is by no means perfect. The purpose of this study is to present a prediction model of movie scenario's box office hit based on human brain processing mechanism. In order to derive patterns of visual, auditory, and cognitive stimuli on the time spectrum of box office animation hit, this study applied Weber's law and brain mechanism. The results are as follow. First, the frequency of brain stimulation in the biggest box office movies was 1.79 times greater than that in the failure movies. Second, in the box office success, the cognitive stimuli codes are spread evenly, whereas in the failure, concentrated among few intervals. Third, in the box office success movie, cognitive stimuli which have big cognition load appeared alone, whereas visual and auditory stimuli which have little cognitive load appeared simultaneously.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.859-869
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• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.

• Knowledge Management Research
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• v.16 no.2
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• pp.213-226
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• 2015

This study investigates the effects of the volume and valence of the movie reviews on the weekly box-office revenues. Existing literature shows that only the volume of movie reviews influences the box office results, but not valence. However, it has limitations in that it includes only the positivity or negativity ratio of the reviews, not the strength of the valence. In order to overcome such limitations, this study includes the degree of valence. This study used approximately 1.3 million reviews about 300 movies as the sample which was collected from a movie review site in an online portal, that is, movie.naver.com. SPSS was used to test the proposed model. The results of this study show different findings compared to those of the previous studies. First, the volume of movie reviews has been a consistent predictor of the box office success throughout the study periods. Second, the ratio of positive reviews has no impact on the first week's results, but shows significant effects on the box office results during the second week. Third, regarding the degree of positivity or negativity in reviews, the degree of positivity has a significant impact on the box office results only during the first week, while the degree of negativity does not have any significant effects on the results. However, from the second week, the situation is reversed; that is, only the degree of negativity has a significant impact on the box office results, but not the positivity.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.217-226
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• 2017

Korea has the tenth largest film industry in the world; however, detailed analyses using the factors contributing to successful film commercialization have not been approached. Using big data, this paper analyzed both internal and external factors (including genre, release date, rating, and number of screenings) that contributed to the commercial success of Korea's top 10 ranking films in 2011-2015. The authors developed a WebCrawler to collect text data about each movie, implemented a Hadoop system for data storage, and classified the data using Map Reduce method. The results showed that the characteristic of "release date," followed closely by "rating" and "genre" were the most influential factors of success in the Korean film industry. The analysis in this study is considered groundwork for the development of software that can predict box-office performance.

• 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.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.1-13
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• 2019

This study examines the impact of factors that affect Hollywood movies' box office performance, using a sample of 149 movies that were ranked number 1 on the first week of release between 2014 and 2018. Such factors as production budget, the number of opening screens, the valence and volume of both critics and netizens, rating, distributor's power, remake, high season, genre are introduced to analyze box office performance. The result shows that the determinants of the US box office and the total box office are different. Considering the difference in budget by genre, the number of opening screens is not influential in the US box office performance of all genres with production budget of less than $100 million. The result suggests the need to refine more elaborated models for future research on box office performance.

• 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.13 no.7
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• pp.51-61
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• 2013

This Paper aims to explore the key factors that drive box-office success in Korean, Chinese and Japanese film market regarding to the storytelling of the movies. At first, I researched the top 20 movies at the box office of Korean, Chinese and Japanese film market from 2007 to 2011.And then I tried to figure out the key factors for the success and the differences among those countries. Usually the purpose of the movie planning in Korea is for the domestic market. In the profit structure of Korean film industry, it is really hard to focus on the overseas film market. In Chinese film market, recently it has been changed a lot especially film genre. The movie liked to watch is melodrama, romantic comedy movie It is being changed to the comedy genre from the war and martial arts movie. And it is emphasized the importance of the scenario in the film production. They want to watch their lives like a real and dreams at the movie and the movie tries to show this trend. In Japanese film market, they made movies in which TV drama, animation and cartoon according to the O.S.M.U. strategy. The movies like to watch are the blockbuster movies, horror movies, thriller movies and melodrama. Comparing with Korea and China, they like movies which show more fantastic and fairy tale imagination.