• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.289-296
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• 2015

The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.9-17
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• 2021

Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.157-171
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• 2014

As electronic word of mouth plays an important role in purchase behavior among consumers, the number of studies on the impact of electronic word of mouth is rapidly increasing. Nevertheless, it is difficult to discover comparative studies on the mass media which had a great impact on consumer's purchase behavior before the impact of electronic word of mouth becomes greater versus the social media where electronic words of mouth are created and distributed. It is considered that it seems to be necessary to find an appropriate mutual supplement point between the media designed for a successful marketing by comparing and analyzing the existing mass media versus the social media, major media for electronic word of mouth. Therefore, this study aims to compare and analyze the impact of comments on movie revenue in the representative forms of mass media such as online news and social media blogs. In particular, this study also considers an appropriate media for promoting movies by period by comparing and analyzing the two media before and after film release. For analysis, this study collects the information on the number of comments on online news and blogs in 70 Korean movies released in 2011 and 2012 from five weeks before film release to eight weeks after film release on a daily basis via Naver. This study also collects the information on the movie revenue using the statistical data of movie industry from Korean Film Commission. As a result of empirical data analysis, it is found that the two media showed no difference in movie revenue before film release, but after film release, the impact of blogs was more significant than that of online news.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.494-507
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• 2020

This study proposed the movie recommendation system based on the user's personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. The main idea is to solve the problem of cold-start and to increase the accuracy in the recommendation system further instead of using the basic technique that is commonly based on the behavior information of the users or based on the best-selling product. The personal information of the users and their relationship in the social network will divide into the various community with the help of the k-clique method. Later, the ranking measure method that is widely used in the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for new users. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed method offers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three most successful methods used in this experiment, and that it can solve the problem of cold-start.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.159-168
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• 2011

As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.292-297
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• 2014

As a Social Network Service (SNS) has become an integral part of our everyday lives, millions of users can express their opinion and share information regardless of time and place. Hence sentiment analysis using micro-blogs has been studied in various field to know people's opinion on particular topics. Most of previous researches on movie reviews consider only positive and negative sentiment and use it to predict movie rating. As people feel not only positive and negative but also various emotion, the sentiment that people feel while watching a movie need to be classified in more detail to extract more information than personal preference. We measure sentiment distributions of each movie from tweets according to the Thayer's model. Then, we find similar movies by calculating similarity between each sentiment distributions. Through the experiments, we verify that our method using micro-blogs performs better than using only genre information of movies.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.69-74
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• 2018

Through the convergence of Internet technology and various information technologies, it is possible to collect and process vast amount of information and to exchange various knowledge according to user's personal preference. Especially, there is a tendency to prefer intimate contents connected with the user's preference through the flow of emotional changes contained in the movie media. Based on the information presented in the script, the user seeks to visualize the flow of the entire emotion, the flow of emotions in a specific scene, or a specific scene in order to understand it more quickly. In this paper, after obtaining the raw data from the movie web page, it transforms it into a standardized scenario format after refining process. After converting the refined data into an XML document to easily obtain various information, various sentences are predicted by inputting each paragraph into the emotion prediction system. We propose a system that can easily understand the change of the emotional state between the characters in the whole or a specific part of the various emotions required by the user by mixing the predicted emotions flow and the amount of information included in the script.

• Journal of Distribution Science
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• v.13 no.10
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• pp.109-114
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• 2015

Purpose - The movie market has the characteristics of being a perfectly competitive market as well as a pure monopolistic market at the same time. This is because there are competitors in the industry but prices, although not fixed, have not changed a lot. Price competition may not have spread, but the competition is focused on artistic value, and the degree of box office success is most important. The artistic value is determined in the course of the production process. However, the degree of box office success is dependent upon the marketing manager. The marketing strategy represents the difference in the standard or quality of the movie. Inherently, the marketing manager adopts the entertainment strategy based on the quality of the foundation of the completed movie. At this time, the marketing manager knows the pertinent information (high quality/low quality) regarding the movie. This research study tries to reveal what should be the reasonable movie marketing expense, dependent on the quality of the movie. Research design, data, and methodology - Using a game scenario with different market players, the goal of the research analysis is to find out the following. First, the marketing expense is determined to maximize the profits after film production. Second, after the production costs are already committed, the manufacturer gets to choose the marketing level. At this time, there will be a profit maximization point, considering the competition. The premise of the research is as follows: if it is a good movie of quality, positive word of mouth increasing the audience continuously slows down the speed of the demand curve. If the movie quality is bad, the negative word of mouth decreasing the audience gradually hastens the speed of the demand curve. On the marketing side, when the manufacturer invests heavily in the marketing expense of the movie, consumer expectations increase to drive up the audience numbers. On the other hand, it is difficult to improve the profits excessively. When the manufacturer invests in marketing a little bit, the marketing expense is only relatively committed, therefore a lot of demand cannot be gained. Results - If a fixed market share is in a competitive situation, a low quality manufacturer expends relatively more marketing expense. If the situation assumes two manufacturers spend the same for the cost of production, the high quality manufacturer takes more profit. If the manufacturer expends less marketing budget to save costs, the optimum profit cannot be achieved since the other party (opponent) grabs the initial market share. Conclusions - In conclusion, investment is essential for market share to increase. We must refrain from a zero-sum game and have models where the game participants pursue the creative profits together. In the current film industry, there is the dominating logic of winner and loser but we have to create a film industry environment where the participants can be altogether satisfied and live together.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.141-149
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• 2010

The success of movie 'Avatar' make people be interested in 3D stereoscopic movie, and government and 3D industry acknowledge that it is another opportunity to develop 3D market since 1920s. However, despite much interest little research to evaluate the effect of 3D stereoscopic exists. The present research aimed to disclose 3D effect compared to 2D by assumption of the importance of 3D stereoscopic and little evaluation to that as well. When audience are exposed to 3D stereoscopic, many outcomes are supposed to be differentiated from when to 2D. From this hypothesis, this paper examined mood, attitude, and presence as dependent variables. Using polarized stereoscopic projection display, 30 participants watched 2D and another 30 watched 3D stereoscopic movie which were the same content. On conclusion, the two groups were not significantly different and this involved much insight.