• Journal of The Korean Association of Information Education
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• v.11 no.4
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• pp.471-482
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• 2007

These days UCC(User-created content) is being made and shared increasingly in entertainment and sports area, but its life cycle seems to be very short and the cases that it is used for an education or a learning purposes are not common yet. In this study a new methodology is suggested for adapting a UCC to a distance education. A teacher upload the movie that he or she made for the distance education system, so the students can carry out the self-centered learning procedure. After that, the students send their own movie files to the teacher, and get a feedback from the teacher as a evaluation of the course. In this study a distance education system was established as a prototype, and a short bamboo flute class was chosen for this study from the specialty developmental education program of the elementary school. According to the result of the questionnaire the students thought that their performance skill was improved a lot and they were satisfied with the learning program and the method of evaluation. They also answered that their skills dealing with a camera, a camcorder and a computer got much better. Moreover, most of the students thought that the relationships with their friends and their parents got better also because they spent lots of time together making and watching the movie files for this education program.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.35-47
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• 2020

Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73^rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.131-138
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• 2013

In this paper, I propose and evaluate the method that classifies emotional type of characters with their emotional words. Emotional types are classified as three types such as positive, negative and neutral. They are selected by classification of emotional words that characters speak. I propose the method to extract emotional words based on WordNet, and to represent as emotional vector. WordNet is thesaurus of network structure connected by hypernym, hyponym, synonym, antonym, and so on. Emotion word is extracted by calculating its emotional distance to each emotional category. The number of emotional category is 30. Therefore, emotional vector has 30 levels. When all emotional vectors of some character are accumulated, her/his emotion of a movie can be represented as a emotional vector. Also, thirty emotional categories can be classified as three elements of positive, negative, and neutral. As a result, emotion of some character can be represented by values of three elements. The proposed method was evaluated for 12 characters of four movies. Result of evaluation showed the accuracy of 75%.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.82-88
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• 2016

In this paper, we propose an intelligibility enhancement algorithm for multimedia contents using spectral shaping. The dialogue signals is essential to understand the plot of audio-visual media contents such as movie and TV. However, the non-dialogue components as like sound effects and background music often degrade the dialogue clarity. To overcome this problem, this paper tries to improves the dialogue clarity of audio soundtracks which contain important cues for the visual scenes. In the proposed method, the dialogue components are first detected by soft masker based on speech presence probability (SPP) which is widely used in speech enhancement field. Then, extracted dialogue signals are applied to the spectral shaping method. It reallocate the spectral-temporal energy of speech to enhanced the intelligibility. The total energy is maintained as unchanged via a loudness normalization process to prevent saturation. The algorithm was evaluated using the modeled and real movie soundtracks and it was shown that the proposed algorithm enhances the dialogue clarity while preserving the total audio power.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.107-121
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• 2005

Recently, many researches on recommendation systems and collaborative filtering have been proceeding in both research and practice. However, although product items may have multi-valued attributes, previous studies did not reflect the multi-valued attributes. To overcome this limitation, this paper proposes new methodology for recommendation system. The proposed methodology uses multi-valued attributes based on clustering technique for items and applies the collaborative filtering to provide accurate recommendations. In the proposed methodology, both user clustering-based CBR and item attribute clustering-based CBR technique have been applied to the collaborative filtering to consider correlation of item to item as well as correlation of user to user. By using multi-valued attribute-based clustering technique for items, characteristics of items are identified clearly. Extensive experiments have been performed with MovieLens data to validate the proposed methodology. The results of the experiment show that the proposed methodology outperforms the benchmarked methodologies: Case Based Reasoning Collaborative Filtering (CBR_CF) and User Clustering Case Based Reasoning Collaborative Filtering (UC_CBR_CF).

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.121-128
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• 2015

A given product's online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews' sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.690-701
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• 2013

With the increasing adoption of social network services (SNS), the cultural and art industry is also embracing SNS as an important tool of marketing. Users can share various cultural experiences on SNS easily, and companies can analyze SNS to understand the users for effective marketing. Based on this background, this study analyzed users' behavior and attitude about movies according to SNS usage types. Users of SNS were surveyed and clustered into 'information seekers', 'fun seekers', and 'relationship seekers'. Next, the behavior of the users in each cluster was compared regarding information search about movies, preferred online advertisement channels, and post-watching behavior. The results showed that the SNS usage type has significant relationship with the behavior and attitude about movies. This suggests that movie industry can establish effective online marketing strategy by analyzing SNS usage of users.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.134-141
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• 2007

In 3D animation rendering process, Although we can render the output as a movie file format, most productions use image sequences in their rendering pipelines. This Image Sequence rendering process is extremely important step in final compositing in movie industries. Although there are various type of making image rendering processes, TGA format Is one of most widely used bitmap file formats using in industries. People may ask TGA format is most suitable for in any case. As we know 3D softwares have their own image formats. so we need to testify on this. In this paper, we are going to focus on Alias' 3D package software called MAYA which we will analyze of compressing image sequence, Image quality, supporting Alpha channels in compositing, and Z-depth information. The purpose of this paper is providing to 3D Pipeline as a guideline about effective image sequence format.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.65-73
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• 2020

Viral video advertising is being used in the advertising and film industry for pre-promotion of certain products or pre-release films which has a lot of effect on investment. An analysis of viral video recognition is needed to predict future development directions. In response, the study conducted a survey on viral videos, focusing on college students in their 20s, who are the most exposed to advertisements and movies. Through this, the survey was conducted on recognition of viral videos, memorable viral videos, satisfaction level, message propagation method, positiveness of viral videos, expected future development, and desired viral video type. The survey showed that viral video recognition was 16.7% and the most memorable viral video; the "Let it Go" viral video from the movie "Frozen" was 69.1 %, according to the survey. The satisfaction level was not high at 31.2 %, and 73.5% of people sent messages to others after watching viral videos, which was very high. Negative opinions on viral videos were low at 13.7 %. 64.5% of the surveyors said the future of the viral videos would "develop" and 6.7% said would "not develop."

• Korean journal of communication and information
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• v.39
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• pp.355-384
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• 2007

The objective of this study is to see the characteristics of Korean films exported in Japan: preferred genres, casting of Korean wave star, major distributors' releases, number of spectators in domestic market. According to the analysis, first of all, Japanese movie goers prefer Korean melo-movies compared with Korean spectators who tend to like comics. It is also proven that Korean wave stars have been cast largely in Korean films exported in Japan. Furthermore, the big hit Korean films that have been distributed by 3 majors have been preferred by Japanese importers. That means the brand power of Korean distributors and release performance in domestic market can influence directly the exportation of films.