• Proceedings of the Korean Society for Language and Information Conference
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.481-488
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• 2007

In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2004년도 춘계종합학술대회
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• pp.208-211
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• 2004

Hundred numbers of Special effects in animation and movie are essentionally used. Owing to the rapidly growth of Production method and special techniques, we can produce a real effect such as real scene. The aim of special effect is to generate refined scene, which we can't distinguish one from another. In this paper, New trend of special effects in animation and movie are compared and investigated the extension methods. And also we prospect the development direction of special effect.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1141-1155
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• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.322-326
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• 2023

We propose a method of movie recommendation that involves an algorithm known as spectral bipartition. The Social Network is constructed manually by considering the similar movies viewed by users in MovieLens dataset. This kind of similarity establishes implicit ties between viewers. Because we assume that there is a possibility that there might be a connection between users who share the same set of viewed movies. We cluster users by applying a community detection algorithm based on the spectral bipartition. This study helps to uncover the hidden relationships between users and recommend movies by considering that feature.

• KIPS Transactions on Computer and Communication Systems
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• 제9권1호
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• pp.17-24
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• 2020

In this paper, we present a movie rating prediction architecture based on a convolutional neural network (CNN). Our prediction architecture extends TextCNN, a popular CNN-based architecture for sentence classification, in three aspects. First, character embeddings are utilized to cover many variants of words since reviews are short and not well-written linguistically. Second, the attention mechanism (i.e., squeeze-and-excitation) is adopted to focus on important features. Third, a scoring function is proposed to convert the output of an activation function to a review score in a certain range (1-10). We evaluated our prediction architecture on a movie review dataset and achieved a low MSE (e.g., 3.3841) compared with an existing method. It showed the superiority of our movie rating prediction architecture.

• Journal of Information Technology Applications and Management
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• 제27권1호
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• pp.187-198
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• 2020

With the development of technology and the creation of an entertainment environment for leisure, various marketing strategies are being used in the film industry. Among them, the use of the credit cookie of ending credits was very effective in producing the series. The ending credit is the time it takes to show the names of the people who made the movie, which is meaningless to the audience. There is a cost to produce a ending credit but It wasn't made because no revenue was generated. The credit cookie was inserted into this ending credit area, which brought new pleasure to the audience. Most of them were epilogue images showing the story behind the movie, NG images showing the NG situation during film production, and In videos mentioned in the movie but not shown in the movie itself. As various ideas about credit cookie were connected with marketing, a series movie and a spin-off foretelling the derivative works after the screening work were produced and have a new meaning. As a result, the time of ending credits, which had no commercial value, became the methodology of the most powerful promotional strategy. Looking at the difference between live-action film and animation in producing such credit cookie, unlike live-action films that edit the remaining parts after shooting, the NG video of the animation has a lot of time and money to produce. So, it hasn't try very well, and it seems to have been actively produced when moving from 2D animation to 3D animation. This is because 3D animation, which has already been modeled, can create new NG scenes by simply adding animating based on the layout of the created scene. Since it is possible to produce an episode movie at a low cost and time, and to use the scenes of the movie after the production, it will be necessary to strategically produce credit cookie for promotion in animation.

• The Journal of the Korea Contents Association
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• 제10권7호
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• pp.420-428
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• 2010

The advancement of the Science for complex systems enables the analysis of many social networks. We constructed and analyzed a Korean movie star network as one of social networks, based on the 100 Korean movie selection for a main data source. Until now, the research trend has been the structural analysis of network, focused on link numbers, such as degree, betweenness and clustering coefficient. But it is time that the research is not limited by the structural analysis of networks only. Rather, the research goal should be aimed to an information analysis, performed by identifying and analyzing central modules that are regarded as the core of complex networks, using k-core analysis method. In this research, we constructed a network of movie stars who have appeared in 100 Korean movie selection, provided by Korean movie database, also we analyzed its core modules with and without weights, and the trend of seasonal expansion of the network. We expect our findings can be used as the basic data applicable to a model for understanding of the expansion and evolution of networks.

• Journal of Internet Computing and Services
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• 제16권2호
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• pp.41-48
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• 2015

On movie review sites, movie ratings are determined by netizens' subjective judgement. This means that inconsistency between ratings and opinions from netizens often occurs. To solve this problem, this paper proposes sentiment sentence sets which affect movie evaluation, and apply sets to comments to infer ratings. Creation of sentiment sentence sets is consisted of two stages, construction of sentiment word dictionary and creation of sentiment sentences for sentiment estimation. Sentiment word dictionary contains sentimental words and its polarities included in reviews. Elements of sentiment sentences are combined with movie related noun and predicate from words sentiment word dictionary. In this study, to make correspondence between polarity of sentiment sentence and sentiment word dictionary, sentiment sentences which have different polarity with sentiment word dictionary are removed. The scores of comments are calculated by applying averages of sentiment sentences elements. The result of experiment shows that sentence scores from sentiment sentence sets are closer to reflect real opinion of comments than ratings by netizens'.

• Journal of Digital Convergence
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• 제14권8호
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• pp.453-460
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• 2016

This study aimed to compare the systems of ticketing application programs of three major movie theaters, CGV, Megabox, Lotte Cinema, with each other and evaluate the usability, and find problems to figure out user experiences for more convenient mobile ticketing. Experimental subjects with experiences of using apps of the movie theaters were recruited; First, as a primary task, they reserved movie tickets with each of the movie theater apps; Second, they had in-depth interviews with questions based on the model of Creating Pleasurable Interfaces by Stephen Anderson. As a result, users preferred the composition with information in order in overall, in which ticketing process went smoothly. In particular, users were more satisfied with convenient payment applications. Therefore, as an improving way, it is required to design an interface for users to recognize at a glance and a payment system within an app, not to design separate payment system out of the app. I hope this study will help actively conduct researches to maximize the usability in a way to reserve movie tickets through smartphone apps.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.463-464
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• 2021

본 논문은 관리자가 영화 및 영화관을 등록하고 등록된 데이터들을 바탕으로 상영 정보를 등록할 수 있게 한다. 사용자는 등록된 영화 및 영화관들의 정보들을 볼 수 있으며 필요에 따라서 등록된 상영 정보를 통해 영화를 예매할 수 있게 하는 시스템이다. 사용자들은 영화를 예매할 때 현재 좌석의 상황을 실시간으로 볼 수 있게 구현하였다.