• Korean Management Science Review
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• v.25 no.1
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• pp.123-138
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• 2008

Recently, DMB service markets experience a rapid change with terrestrial DMB test-broadcasting for the nation-wide coverage and paid interactive data broadcasting being offered utilizing TPEG and BIFS technologies. This warrants a reexamination of a consumers' adoption intentions for DMB service. This paper uses a survey data set to analyze DMB adoption intentions and the choice between terrestrial DMB and satellite DMB services according to preferred contents and other media usage characteristics. Empirical results show that consumer who prefer TV, music, and movie contents are more likely to adopt DMB service, whereas consumers with high intentions for HSDPA subscription are less likely to adopt DMB service. This implies that continuing development of killer application and the analysis of substitutes or complements of other media are crucial for the increase of DMB adoption intentions. It is found that the more consumers prefer sports, movies and entertainment/game and put higher values in the quality of the contents, the more likely they adopt satellite DMB service. Meanwhile, the more consumers prefer TV, drama and news contents, and are sensitive to the subscription fees, they are more likely to adopt terrestrial DMB service. Therefore, it seem that consumers' DMB adoption between terrestrial and satellite services is crucially related with types and characteristics of contents offered.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.721-726
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• 2006

인터넷 게시판을 통한 커뮤니케이션에서는 상호작용의 결과물이 지속적으로 남아 있어, 상호작용의 당사자뿐 아니라 나중에 참여하는 다른 이용자들의 게시판에 대한 지각에도 영향을 미치게 된다. 따라서 인터넷 게시판에서의 이용자들 간의 상호작용은 게시판의 기술적 형식이나 주제뿐 아니라 게시판에 있는 기존 이용자들의 메시지를 읽고 받은 인상에 따라서도 달라질 수 있다. 본 연구에서는 게시판의 익명성과 메시지의 전문성을 독립변인으로 한 $2{\times}2$ 실험설계에 따라 가상적 CMC 상황에서 피험자들이 영화 리뷰 게시판의 글을 읽도록 하였다. 게시판 기존 이용자 집단에 대해 받은 인상의 긍정성, 모호성, 전문성, 피험자 자신과의 지각된 유사성 및 해당 게시판에 대한 참여의사를 측정하였다. 결과, 전문성이 높은 메시지를 읽은 피험자들은 게시판의 기존 이용자들이 보다 전문적이라는 인상을 받았으며, 해당 게시판에 참여하고자 하는 의사를 더 많이 표시했다. 또한 기존 이용자들에 대한 인상의 긍정성과 유사성, 참여의사 사이에는 상관관계가 있었다. 이 결과는 인터넷 게시판 이용이 기술적 요인뿐만 아니라 게시판의 기존 이용자 집단에 대한 인상에 따라서도 달라질 수 있음을 시사한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6080-6096
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• 2019

Text classification is one of the fundamental techniques in natural language processing. Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification. Traditional text classification methods are used to extract features and then classify them. However, traditional methods are too complex to operate, and their accuracy is not sufficiently high. Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem. In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus. Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.117-123
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• 2018

This article is to investigate the possibility that project-based classes introducing mobile phones can replace the monotony of traditional classes led by teachers as well as they can encourage students to take active part in the classes to some extent. The students in groups choose a genre for their own video projects (e.g., movie, drama, news, documentary, and commercial) and produce the video contents using a mobile phone for presentation made at the end of a semester. In the sense that the students are allowed to do video-based mobile phone projects, they can work independently outside of class, where time and space are more flexible and students are free from the anxiety of speaking or acting in front of an audience. A mobile phone project consists of around five stages done both in and outside of the classroom. All of these stages can be graded independently, including genre selection, drafting of scripts, peer review and revision, rehearsals, and presentation of the video. Feedback is given to students. After the presentation, students filled out a survey questionnaire sheet devised to analyze students' responses toward preferences and level of difficulty of the project activity. Finally, proposals are made for introduction of a better mobile phone-based project classes.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.33-40
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• 2017

This article is to investigate the possibility that project-based classes introducing mobile phones can replace the monotony of traditional classes led by teachers as well as they can encourage students to take active part in the classes to some extent. The students in groups choose a genre for their own video projects (e.g., movie, drama, news, documentary, and commercial) and produce the video contents using a mobile phone for presentation made at the end of a semester. In the sense that the students are allowed to do video-based mobile phone projects, they can work independently outside of class, where time and space are more flexible and students are free from the anxiety of speaking or acting in front of an audience. A mobile phone project consists of around five stages done both in and outside of the classroom. All of these stages can be graded independently, including genre selection, drafting of scripts, peer review and revision, rehearsals, and presentation of the video. Feedback is given to students. After the presentation, students filled out a survey questionnaire sheet devised to analyze students' responses toward preferences and level of difficulty of the project activity. Finally, proposals are made for introduction of a better mobile phone-based project classes.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.90-99
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• 2016

Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.750-758
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• 2017

Korean film actors and staff participated in Incheon(Terence Young, 1981) Hollywood filmmaking workforces had taken in South Korea. According to the dictation of the elders who participated in the making of the film Incheon, through the movie they were able to experience Hollywood film production system and said it had an impact on Korea filmmaking system. It is the reason that this study tracks the historical traces of the almost forgotten film. This article analyzed an expert assessment and recognition of the audience about the film Incheon produced by the Hollywood production system. Expert review was utilized in such press articles and tomes about the actors and staff who participated in the film. Also, the audience rating was analyzed in terms of the recognition at the theater or watching cable TV, Internet etc. Evaluation of expert in Incheon was monotonous but negatively red, I could confirm that the viewers recognized in various ways, rather than evaluated the film.

• Korean Journal of Health Education and Promotion
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• v.30 no.5
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• pp.1-13
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• 2013

Objectives: While many studies have been conducted on whether smoking scenes in films actually affect audience members' smoking, a comprehensive conclusion has yet to be derived. This study systematically reviewed the effect of smoking scenes in films on audience members' attitudes, beliefs, and actions. Methods: We analyzed a total of 146 studies searched on PubMed and PsycINFO (41 qualitative studies, 72 cross-sectional studies, 20 longitudinal studies, and 13 experimental studies). Results: Whereas qualitative studies have only demonstrated that audience members tend to perceive smoking scenes in films not as negative information but as positive information, cross-sectional studies have reported a significant association between smoking scenes and smoking behavior notwithstanding the problems of classifying the groups studied and measuring the degree of exposure. Through follow-up observations, longitudinal studies have reported that such media exposure can serve as a predictor of future smoking. Finally, with exposure and confounding variables under control, experimental studies have confirmed that smoking scenes in films indeed affect audience members' attitudes, beliefs, and actions regarding smoking. Conclusions: Scenes of actors and actresses smoking can be imitated or learned through audience members' immersion and identification and reproduce positive images that may render smoking socially acceptable.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.