• Asia Marketing Journal
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• v.11 no.4
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• pp.21-48
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• 2010

This article analyzes film performances in the Korean movie market utilizing three-stage models that incorporate available information in three different stages of the movie life cycle, i.e., at the time of its release, at the end of the first week, and at the end of its life cycle. Based on the premise that the performance of a movie is affected principally by factors of scale, evaluation, and competition, we attempted to ascertain the effects on these factors on performances, and how they differ in different stages. Also, by analyzing domestic and foreign movies released in Korea separately, we were able to compare the different effects of the three factors on the performances of the two categories of movies. Additionally, our movie performance models incorporated herding behavior among the customers. Our results demonstrate that herding is prominently observed after the first week only for domestic movies. In general, the scale factor has been shown to be most important for movie performances in all stages. For foreign films, it is particularly critical for the first week and total performances. Whereas the evaluation factor influences domestic film performance more strongly at the screen choice stage, it affects the performance of foreign films more strongly in the later stages of the life cycle. As compared to foreign films, domestic film performance appears to be more sensitive to the competition factor. We also discuss the effects of covariates such as genre and symbolicity on movie performance.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.177-193
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• 2015

A great number of customers, who want to watch movies usually check out online reviews before choosing what to watch a movie. The most representative online media that customers consult are portal sites and SNS (Social Network Service). Although there have been numerous studies on online eWOM (e-Word of Mouth) and the effects of online media in businesses, it remains a question that which media is best for WOM (Word of Mouth) when selecting movies. This research examines customer's intention for consulting eWOM and for watching movies according to the number and tendency of online replies. We have compared portal sites and SNS about information of movie. The study shows that a large number of positive replies can affect the intention for WOM and choosing movies. Facebook has more influence than portal sites when choosing what to watch when replies consist of large and positive comments. However, there is no difference between the two types of media when they consist of negative comments.

• Asia pacific journal of information systems
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• v.25 no.3
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• pp.563-578
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• 2015

Forecasting daily box-office performance is critical for planning the distribution of marketing resources, and by extension, maximizing profits. For certain movies, the number of viewers increases rapidly at the beginning of their theatrical run, and the increments slow down later. Other movies are not popular in the beginning, but the audience sizes grow rapidly afterward. Thus, the audience attendance of movies grow in different trajectories, which are influenced by various factors including marketing budget, distributors, directors, actors, and word of mouth. In this paper, we propose a method for predicting the daily performance trajectory of running movies based on the hierarchical linear model. More specifically, we focus on the effect of online word of mouth on the shape of the growth curves. We fitted the mean trajectory of the cumulative audience size as a cubic function of time, and allowed the intercept and slope to vary movie-to-movie. Moreover, we fitted the linear slope with a function of online word of mouth predictors to help determine the shape of the trajectories. Finally, we provide performance predictions for individual movies.

• ETRI Journal
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• v.36 no.4
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• pp.581-590
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• 2014

This study investigates the impact of tweets on box office revenue. Specifically, the study focuses on the times when tweets were written by examining the impact of pre- and post-consumption tweets on box office revenue; an examination that is based on Expectation Confirmation Theory. The study also investigates the impact of intention tweets versus subjective tweets and the impact of negative tweets on box office revenue. Targeting 120 movies released in the US between February and August 2012, this study collected tweet information on a daily basis from two weeks before the opening until the closing and box office revenue information. The results indicate that the disconfirmation that occurs in relation to the total number of pre-consumption tweets for a movie has a negative impact on box office revenue. This premise suggests that the formation of higher expectations of a movie does not always result in positive results in situations where tweets on perceived movie quality after watching spread rapidly. This study also reveals that intention tweets have stronger effects on box office revenue than subjective tweets.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.49-55
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• 2019

The character dialogue created by AI is unnatural when compared with human-made dialogue, and it can not reveal the character's personality properly in spite of remarkable development of AI. The purpose of this paper is to classify characters through the linguistic style and to investigate the relation of the specific linguistic style with the personality. We analyzed the dialogues of 92 characters selected from total 60 movies categorized four movie genres, such as romantic comedy, action, comedy and horror/thriller, using Linguistic Inquiry and Word Count (LIWC), a text analysis software. As a result, we confirmed that there is a unique language style according to genre. Especially, we could find that the emotional tone than analytical thinking are two important features to classify. They were analyzed as very important features for classification as the precision and recall is over 78% for romantic comedy and action. However, the precision and recall were 66% and 50% for comedy and horror/thriller. Their impact on classification was less than romantic comedy and action genre. The characters of romantic comedy deal with the affection between men and women using a very high value of emotional tone than analytical thinking. The characters of action genre who need rational judgment to perform mission have much greater analytical thinking than emotional tone. Additionally, in the case of comedy and horror/thriller, we analyzed that they have many kinds of characters and that characters often change their personalities in the story.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.51-58
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• 2009

In this paper. we propose search algorithm using Low Peak Feature of audio and image color value by which similar movies can be identified. Combing through entire video files for the purpose of recognizing and retrieving matching movies requires much time and memory space. Moreover, these methods still share a critical problem of erroneously recognizing as being different matching videos that have been altered only in resolution or converted merely with a different codec. Thus we present here a similar-video-retrieval method that relies on analysis of audio patterns, whose peak features are not greatly affected by changes in the resolution or codec used and image color values. which are used for similarity comparison. The method showed a 97.7% search success rate, given a set of 2,000 video files whose audio-bit-rate had been altered or were purposefully written in a different codec.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1996.10a
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• pp.564-567
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• 1996

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.950-962
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• 2015

This paper analyzes the effect of the number of viewers and the number of running screens in the theater, genres of the movies and holdback period on the number of purchases movie VOD contents. In this paper, we deals with the 169 movies VOD consumption data which the digital cable TV subscribers purchased during the third quarter of 2013. As a result of the research, the average number of VOD purchases per each movie is 2,540. Regression analysis proves that the number of running screens and the holdback period are statistically playing a role of significance in movie VOD purchases. But the number of viewers in the theaters is not of significance. By genres, the result is shown as in this order: SF/fantasy 8,401 > dramas 4,011 > comedies 2,011 > action 1,789 > animation 1,138 > love story/melo 1,119 > horror/thriller 770 > erotic movies 636.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.1020-1026
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• 2017

Korean movies are now entering the global market without staying in the domestic market. Especially, despite the fact that the foreign films are imported and opened to the domestic market very much, there are more domestic films that have succeeded in box office success. In this paper, we try to clarify the characteristics of the voices of movies in order to feel horror among various genres of domestic movies. For this reason, the criterion for the success of the movie is the number of paying audiences, so differences of the voice of the characters of the horror movie that succeeded to hit the box office and the voice of the characters of the horror movie that failed to hit the box are analyzed for verifying the success conditions in voice. In addition, we would like to suggest what kind of voice should be used in order to succeed in the horror movie from the voice point of view.

• 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.