• Journal of Multimedia Information System
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• v.7 no.2
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• pp.141-146
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• 2020

Cine-Hangeul is a program that can predict the running time of a movie based on the screenplay before production. This paper seeks to verify the prediction reporting function of Cine-Hangeul, which is the standard Korean screenplay format. Moreover, this paper presents a method to increase the accuracy of the Cine-Hangeul reporting function. The objective of this paper is to offer a correction method based on scientific evidence because the current Cine-Hangeul reporting function has many errors. The verification process for five scenarios and movies confirmed that the default setting value of Cine- Hangeul's screening time prediction reporting was many errors. Cine-Hangeul analyzes the amount of textual information to predict the time of the scene and the time of the dialogue and helps predict the total time of the movie. Therefore, if a certain amount of text information is not available, the accuracy is unreliable. The current Cine-Hangeul prediction report confirms that the efficiency is high when the scenario volume is about 90 to 100 pages. As a result, prediction of screening time by Cine-Hangeul, a Korean scenario standard format program, confirmed the verification that it could secure the same level of reliability as the actual screening time by correcting the reporting settings. This verification also affirms that when applying about 50 percent of the basic set of screening time reporting, it is almost identical to the screening time.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.1-7
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• 2008

Due to the development of ubiquitous computing, a wide variety of information is being produced and distributed rapidly in digital form. In this excess of information, it is not easy for users to search and find their desired information in short time. In this paper, we propose the personalized item recommendation using the image based filtering. This research uses the image based filtering which is extracting the feature from the image data that a user is interested in, in order to improve the superficial problem of content analysis. We evaluate the performance of the proposed method and it is compared with the performance of previous studies of the content based filtering and the collaborative filtering in the MovieLens dataset. And the results have shown that the proposed method significantly outperforms the previous methods.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.58-63
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• 2020

In literature it is not only the meaning of words which matters, but also their texture matters i.e. their rhythm, colour and style are relevant and none of these natures can be reduced to an item of information. The texture is also important in film languages, especially the rhythm. In order to make the storytelling rhythm visible, a new concept of 'the Rhythm Chart Analysis Method (RCAM)' has been devised in this research and used for analysis. By analyzing original films and remakes in Korean and Chinese, one can find out that different countries have different storytelling rhythms i.e. the same story can be told in different rhythms. The central idea in Korean films is portrayed at the end, which is a typical characteristic of Korean films. And as the defining moment does not occur suddenly the audience can naturally get immersed into the story. But Chinese films communicate with the audience in a more direct way. It directly mentions characteristics of each actor in such a way that it is telling rather than showing. The information of the movie is given to the audience in the initial stages of the movie. Rhythm is as important as story and information. And through this we can find out the cultural differences from the different storytelling rhythms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.97-99
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• 2021

본 논문에서는 극장 비수기 기간 효율적인 상영을 위한 재개봉 영화 도출과 영화관 입장권 통합 전산망을 기반으로 극장 산업과 OTT 산업에서 제공하는 시청각 콘텐츠의 소비자 선호도를 분석한다. 기존 재개봉 영화는 연휴와 같은 성수기 바로 전 비수기 기간에 집중적으로 상영되고 있다. 즉 재개봉 영화 상영은 대형 영화 개봉 전 공백을 메우기 위해 상영되고 있음을 의미한다. 재개봉 영화는 대부분 예술 영화를 상영하고 연도마다 일정한 수요를 보이고 있다. 이러한 기조는 코로나 19 전까지 변함없이 이어졌으나, 코로나 19 이후 재개봉 영화에 대한 수요가 다른 년도 같은 월에 비해 급증하였다. 영화 산업의 전반적인 침체와 달리 재개봉 영화에 대한 수요는 늘어난 것이다. 코로나 19가 장기화되는 만큼 본 논문에서는 영화관 입장권 통합 전산망 데이터를 중심으로 영화 산업과 OTT 산업 이용자들의 선호 콘텐츠를 분석하고 기존 재개봉 영화와 대조하여 지속적이고 효율적 상영을 위한 재개봉 영화를 제안한다.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.965-976
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• 2016

Recently studies about audio-visual immersion are being carried out due to development of digital video but studies analysing quantitatively the content or climax of video has not carried out. The paper is analysed quantitatively by using shot size, camera angle, camera direction and camera position, objective & subjective which is general component of video expression. We can see the climax effects in the video destroying the principles because there is a rule when they use the component. This thesis analyses shot-list based on video expression in existing movies therefore it can analyse quantitatively several method commonly used in order to make the climax. These suggesting method that can find the part of climax based on analysis of shot-list can be used when you search the specific part of video like long movie or when you search the genre of the movie or when you index the genre. Also it can be very effective in various information services companies offering climax movie which is searched.

• 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 the Korea Society of Computer and Information
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• v.18 no.3
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• pp.119-126
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• 2013

In this paper, we propose and describe the system to visualize the story of contents such as movies and novels. Character-net is applied as story model in order to visualize story. However, it is the form to be accumulated for total movie story, though it can depict the relationship between characters. We have developed the system that analyzes and shows the variation of Character-net and characters' tendency in order to represent story variation depending on movie progression. This system is composed by two windows that can play and analyze sequential Character-nets by time, and can analyze time variant graph of characters' degree centrality. First window has a function that supports to find important story points like the scenes that main characters appear or meet firstly. Second window supports a function that track each character's tendency or a variation of his tendency through analyzing in-degree graph and out-degree. This paper describes the proposed system and discusses additional requirements.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.221-229
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• 2009

The development of the Internet and telecommunication technology has lead to the diversification of the distribution channel of movies. Internet users can easily watch movies through the Internet VOD(Video On Demand) theaters without a restriction of time and space. In this study, we try to understand the intention to use of internet VOD movies using the concepts of Technology Acceptance Model and Flow Model. We also consider the concepts of sensitivity of holdback period and availability of various choice in movie genre, along with demographic factors such as age and gender. Through our study we enhance our understanding on how and when users use the Internet VOD for their movie watching.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.284-286
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• 2021

With the continuous development of the Internet and digital, platforms are emerging to store large amounts of media data and provide customized services to individuals through online. Companies that provide these services recommend movies that suit their personal tastes to promote media consumption. Each company is doing a lot of research on various algorithms to recommend media that users prefer. Movies are divided into genres such as action, melodrama, horror, and drama, and the film's audio (music, sound effect, voice) is an important production element that makes up the film. In this research, based on movie trailers, we extract audio for each genre, check the commonalities of audio for each genre, distinguish movie genres through supervised learning of artificial intelligence, and propose a utilization method for generating metadata in the future.