• Journal of the Economic Geographical Society of Korea
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• v.8 no.1
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• pp.107-130
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• 2005

The purpose of this study were to examine the spatial distribution and locational characteristics of the Internet movie industry, to seize the value chains of the Internet movie industry and distribution structure of the internet movies, and to analyze the vertical-horizontal linkages of the Internet movie firms and their spatial clustering. Recently, the Internet movie industry has developed rapidly due to the development of techniques related to movie contents, the broadband Internet and a wide expansion of the high speed communication network and the increase of demands on movie contents. It has been found that 74$\%$ of the Internet movie industry was concentrated in Seoul. Especially this industry was quite agglomerated in several dongs of Gangnam-gu such as Yoeksam, Nonhyeon, Daechi and Samseung. The proximity of the same or similar business firms was the primary locational factors that influenced on the Internet movie industry, followed by other factors such as convenience of transportation, the reputation of the place, and proximity of technically supporting firms. The Internet movie industry had the valve chain composed of 'contents suppliers $\rightarrow$ contents distributors $\rightarrow$ service providers', However, there were also a complex network of the VOD copyright owner, VOD syndicator, and service providers in each category of the value chain. This research clearly revealed that the localized clustering has been formed with the movie contents providers, technically supporting firms, client firms, and cooperative-affiliated business firms related to the Internet movie industry, Additionally, a very intimate network has been established within the clustering, inducing the enlargement of the market and decrease of costs, the co-sharing of tacit knowledge, and the synergy effect.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.241-255
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• 2013

In this study, the relationship between movie ticket reservation rates and Internet buzz share is analyzed. The correlations between movie ticket reservation rates and Internet buzz share in blogs, Internet cafes, news site, and Internet video in NAVER which is a representative Internet portal in Korea are analyzed empirically. The results show that there are positive correlations between buzz shares and movie ticket reservation rates. In particular, before movies at the box office, the correlations with Internet video is relatively higher than those of other channels, and after movies at the box office, the correlations with blogs and Internet cafe are relatively higher. Also, we can find that the correlations between Internet buzz shares on movies and movie ticket reservation rates are different depending on time lags and Internet channels.

• Asia pacific journal of information systems
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• v.15 no.3
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• pp.223-241
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• 2005

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBIIe COntents Recommender System for Movie(MOBICORS-Movie), which is designed to reduce customers' search efforts in finding desired movies on the mobile Internet. MOBICORS-Movie consists of three agents: CF(Collaborative Filtering), CBIR(Content-Based Information Retrieval) and RF(Relevance Feedback). These agents collaborate each other to support a customer in finding a desired movie by generating personalized recommendations of movies. To verify the performance of MOBICORS-Movie, the simulation-based experiments were conducted. The results from this experiments show that MOBICORS-Movie significantly reduces the customer's search effort and can be a realistic solution for movie recommendation in the mobile Internet environment.

• Asia Marketing Journal
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• v.11 no.2
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• pp.53-72
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• 2009

In the summer of 2008, the movie Dachimawa Lee: Villain, Get on the Express to Hell is waiting for its first release. This movie is based on a short film which was tremendously popular when it was first introduced on the internet in 2000, and the director Ryu Seung Wahn is expecting a mega hit of the movie this summer. Showbox is one of the leading movie distributors in Korea. After a series of blockbuster hits such as Welcome to Dongmakgol, The Host, and The Chaser, Showbox is now in stagnation. Along with sluggish economy in Korea, most of recent movies distributed by them didn't even reach the breakeven. However, expectation on the upcoming movie, Dachimawa Lee, is higher than ever. Marketing department of Showbox has to decide what kind of media channel to choose and which marketing strategy to apply. It needs to make a decision about whether to target the so-called mania group or the general public of much bigger size. In this case study, the distinctiveness of the movie marketing and its process will be discussed. Moreover, the marketing strategies for Dachimawa Lee's success will be examined in the Showbox's point of view.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.61-67
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• 2020

The game has several genres. Actions, RPGs, FPS, and simulations are diverse, and the game that flows around the story is a game genre that feels like watching a movie on the other. Internet game broadcasters can be thought of as playing a movie in a movie theater when they broadcast a story-type game. The difference with the movie theater is that you can get involved in the story by showing your play directly to viewers. As a broadcasting material, story-type games are a good means, but the fact that the story is published on the Internet in terms of game publishers can have a negative impact on sales revenue as viewers can enjoy the story without purchasing the game. The purpose of this study is to analyze the coordination status between producers and internet broadcasters for story-type games that could be copyrighted and suggest ways to move forward.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Journal of Internet Computing and Services
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• v.16 no.2
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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'.

• 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 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.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.125-141
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• 2014

Nowadays, overflowing data produced every second from the internet make people to be difficult to search for the useful information. That's why people have invented and developed unique tools that they get some relevant information. In this paper, the recommender system, one of the effective tools, is used and it helps us to get the useful information that we want by using demographic information to predict new items of interest. The demographic recommender system in this paper computes users' similarity using demographic information, age and gender. So we performed demographic analysis on movie ratings on Internet Movie Database (IMDB) web site that movies are rated by thousands of people, where users submitted a movie rating after they watched a recent popular film. Meanwhile, we can understand that user's ratings, among various determinants of box office, is very essential factor in the study on recommendation of movie. This paper is aimed at analyzing movie average ratings directly given by film viewers, categorizing them into groups by sex and age, investigating the entire group and finding the representative group by examining it with F-test and T-test. This result is used to promote and recommend for the target group only. Therefore, this study is considerably significant as presenting utilization for movie business as well as showing how to analyze demographic information on movie ratings on the web.