• The Journal of Art Theory & Practice
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• no.2
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• pp.169-190
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• 2004

The purpose of this article is to delve into the problems of originality of the artwork by examining issues of reproduction within the contemporary art market. In contemporary arts, especially in terms of art production and consumption, we can't overlook society and its economic structure and its connection with of capitalism. As the purity of art creation has turned into an exchange value, art, especially an object as artwork, has fallen into the status of production in an economic marketing system. Walter Benjamin mainly referred to that point in his thesis Das Kunstwerk im Zeitalter seiner technischen Reproduzierbarkeit, which originated the sociology of plastic arts. This thesis, published in 1936, traced how the artistic functions of photograph and movie had been changed through the social development. His main concerns were movie and photograph but what I am concentrating from his point of view, is that even in the field of plastic arts, the manufacture of reproduction has been practiced as a primary method within the social and political contexts and development. Though I am referring to this in the main body of this article, reproduction in contemporary art strongly needs a new definition since it has been spread all over like a newest virus, not only by collector's personal taste or hut also by commercial circulations of these reproductions to the public. This relates to Benjamin's argument about the value of an exhibition at a museum(Ausstellungswert). Since the function of an artwork has been one of cultural industry, the manufacturing of reproduction raises unexpected problems, such as, the originality of the artwork, the value of an exhibition at a museum, its achievement as documentary and as a territory of art criticism. In this point of view, I want to inquire into the value and criteria of an exhibition in contemporary art through the review of the definitions and the intrinsic attributes of reproduction. Somehow in a broad sense, the reproduction is a product coming out of representation or copy (replica) of an original art work or an model. Therefore, the problems it presents differ from the Simulacre, which is an image without an original one. In terms of the Meanings of reproduction, we can distinguish it as reproductions, copies, and productions. These types of reproductions are not the original artworks reflected by the creative intention of the artists. For example, a publishing company reproduced some of lithographs of Salvador Dali in the 1960s. They are commercial copies in the form of representation or reproduction with no artistic and creative intention of the artist. However, In despite of this theoretical basis, reproductions of the famous artists are still displayed without any verification for of the public's quest for the artworks. Moreover, many commercial companies that are planning to exhibit art works of the world-famous artists only for their profits keep trying to speak ill of and judging by the law the honest art critics' articles which discuss the true values of exhibition. If freedom of expression is one of the ideals of democracy, even the judgment of the originality of the artworks should be freely expressed.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.315-326
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• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Korean Management Science Review
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• v.34 no.3
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• pp.61-77
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• 2017

This study investigates the effect of consumer involvement in the relationship between customer satisfaction and loyalty (which is measured as consumers' repurchase and WOM intentions). Previous research has presented inconsistent implications on the role of consumer involvement in the relationship. Some researchers have presented the empirical results showing that consumer involvement moderates the relationship, whereas others have done the empirical results implying that the consumer involvement does not moderates. In sum, the previous empirical studies have provided the inconsistent empirical results that consumer involvement may be a moderator or a non-moderator in the relationship between customer satisfaction and loyalty. Thus, we start this research with two research questions: "Does consumer involvement has a moderation effect on the relationship between-customer satisfaction and loyalty across various service industries?" and "Can the moderation effect of consumer involvement be detected differently depending on the statistical methods used to probe the moderation effect?" A questionnaire survey was conducted in the context of four service categories: eight service firms for fast food, four firms for family restaurant, four firms for movie theater and four firms for Bakery. Hierarchical regression analyses (moderated regression analyses) and chi-square difference tests were used to identify the role of consumer involvement in the relationship between customer satisfaction and loyalty. The empirical results show that there does not exist the difference in the moderation effects of consumer involvement identified by the two statistical methods (i.e., hierarchical regression analyses vs. chi-square difference tests), however, that there exists the difference in the identified moderation effects of consumer across service categories. In the final section, we summarize the implications and re-interpret the empirical results provided in the previous studies.

• International Commerce and Information Review
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• v.17 no.3
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• pp.73-91
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• 2015

This study investigated how Korean image and cosmetics products image effect by the Korean Wave(Hanllyu) on the Vietnam Customer focus on the cosmetics. Especially, Korean Wave of this study consisted in the movie/drama, K-pop and Korean stars. Korean image and cosmetics brand image was effected by factors related to the Korean Wave which had been deducted, based on preceeding research. A study was conduct in Vietnam where the spread of the Korean Wave content customer bases has been over along time. A total of 295 samples were used for th final analysis. Data analysis consisted of descriptive statistics, Cronbach's alpha, a confirmatory factor analysis, and multi-regression. It is also found that Korean image and cosmetics brand image has affect by Korean Wave. and Cosmetics purchasing intention has affect Korean image and cosmetics brand image.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.17-24
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• 2012

Andre Bazin called the movie frame as "the window open to the world." This expression is close to realization through 3D films. The 'Avatar' released in 2009 was a new turning point for 3D films. Nowadays the theory and information about 3D films is overflowed. It is necessary to find practices and to accumulate data useful in production of 3D films. There are several ways of working to achieve high quality 3D films. In any way that's chosen, there are priorities to be considered to create well-balanced 3D films. The aim of this article is to review primary considerations in film-making and share the technical issues experienced during the production of "Let's go to the amusement park again, Mom." Because the current practical knowledge in making 3D film is shallow, this article will offer a possible reference for further research.

• Korean Journal of Human Ecology
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• v.13 no.6
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• pp.967-983
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• 2004

The purpose of this study is to review Hanliu phenomenon, a kind of social and cultural phenomenon, in China and to analyze its effects on the fashion style of new young generation of China. In this study, Hanliu phenomenon means the enthusiasm of Asian people for Korean mass culture including Korean dramas, pop songs, and fashions from late 1990s. This research adopts two kinds of methods for analyzing the phenomenon: qualitative and quantitative research methods. As a qualitative research method, we analyzed it with several sources of documentaries and audio-visual materials: articles from newspapers and magazines, special TV reports, and documentary movie files from Internet. As a quantitative research method, we surveyed approximately 100 female students of Beijing university and asked how they feel Korean culture and fashions. The Hanliu phenomenon led to the popularity of Korean products as well as general culture of Korea. Also, it influenced Chinese young generation so much that Korean fashion has become prevailing. Such influence on the street fashion of Chinese youths can be summarized in three factors as follows: First, Korean entertainers' fashion is widely imitated. For example, H.O.T-like hairstyles, hip-hop styles, large heel shoes with boots-cut pants, and long-curled permanent hairstyles have been on among Chinese youths. Second, the preference for Korean fashion products has highly increased. The number of stores dealing with Korean fashion products has increased. Even the 'Kim Hee Seen,' a fashion brand named after a famous Korean actress, was introduced. Finally, Korean culture and products have widely been imitated in China as much as the increasing popularity of Korean fashion products. This study reveals that Hanliu phenomenon is widespread in China, and Chinese youths are largely affected by the fashion styles of Korean entertainers. Also, Korean fashion products are largely imitated and benchmarked in China. Hanliu phenomenon is a big chance to approach the fashion market of China, the largest buying power in the world. To make inroads into the Chinese fashion market, we suggest that we need to have our own brand and to make the most of culture, stars, and Internet in marketing. Also, we need a well-planned strategy for a success in the Chinese fashion market.

• International Commerce and Information Review
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• v.12 no.3
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• pp.55-78
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• 2010

This study analyses recent development of digital contents industry. The purpose of this study is to show how the convergence phenomenon is occurring in the digital contents industry. Furthermore, this study examines the influence of digital convergence on the digital contents industry. The characteristics of the digital contents industry falls roughly into three features. To begin with, technical aspect of the industrial feature is that digitalized contents can be used in various digital devices, namely OSMU(One Source Multi Use). The second feature is related to protection of copyright against illegal file sharing and downloading. One final point is that platform for distribution channels has been universal by digital convergence. To sum up, the notable feature of digital contents industry is high value-added. Also, digital contents industry is composed of users, digital device, network, and universal contents. Users are the key component of digital contents industry, who is distinguished from consumers. Digital devices such as mobile phone, PDA can play all kinds of digital contents and make users communicate in two-ways. Portable devices also allow the users to consume digital contents at any place. Digital contents can be distributed by both wire and wireless networks. And most of transactions can be made through networks. There are three key issues about digital convergence. Entry barriers for market become lowered; the age of contents users is changed from old generation to young generation. And the form of contents devices is changing rapidly. Traditional contents field such as movie, music, broadcasting, publishing, animations are combined into one digital contents territory. As a result, this paper suggests that digital convergence phenomenon will be accelerating for the future. According to the result of this study, the advent of digital convergence and e-Commerce will have significant influence on trade of digital contents.

• International Commerce and Information Review
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• v.15 no.2
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• pp.109-128
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• 2013

With the growth of contents business, the expansion of domestic culture contents into global market became active. However, while some field such as game, music and movie have made fine results, education contents has failed to make significant success in global market. Therefore, this study intends to look into a case of Contents Management Institute(CMI), which spread G-Learning into La Ballona Elementary School located in LA. In this case, CMI successfully dealt with diverse difficulties to conduct a G-Learning class in the school and helped to increase students' achievement. Based on analyzing this case, this study suggests three reasons behind the success. First, by separating platform and learning contents in development process, CMI could save the cost in contents development and handle problems swiftly. Second, it could be possible to use human resources efficiently by constucting a support organization. Third, by sharing information and doing persuasion CMI could lead to chain persuasion process among local decision makers.