• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.377-388
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• 2019

The system proposed in this paper was suggested as a big data system that works in the age of artificial intelligence of the 4th Industrial Revolution. The proposed system can be a good example in terms of government 's development of new intelligent big data information system. For example, the proposed system may be introduced into the system of a department as a function of the integration of existing cinema ticket integration network or its networking. For this purpose, the proposed system transmits the user's profile to the film producer or other company, where it is provided as comparison data. Soon, the information is sent to the user-specific characteristic data and then the film-maker will be able to gauge the success of the three elements of the movie's performance, cinematic quality, and break-even point in real time, which are revealed through the movie review that the actual user feels, including the so-called 'new reinterpretation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.79-89
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• 2018

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phoneme-level sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.

• Korean System Dynamics Review
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• v.10 no.2
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• pp.5-27
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• 2009

Multiplex is a representative culture facility of citizens. Therefore, a lot of researches and investment on multiplex are carried out to improve benefits of service suppliers and users. Especially, focused on main services of a theatre such as ticket booking and issuing within multiplex, examination of tickets, admission information of movie screens and screening information inquiry, improvement activities are carried out. However, it is not enough to evaluate on what efficiency the above efforts have in the viewpoint of customer benefits and business. Therefore, this study analyzed value and limit of the newest service of multiplex applying the existing ubiquitous concept(u-multiplex service), and proposed a model and a plan for improving the existing services. The study interviewed with specialists in the related field and applied workshop-shape group interview to 110 university students and simulated service models. The contribution of the study is to analyze the value and limit of the existing multiplex service objectively, and to propose a new service model and plan to improve its limitation. In the future, the study plans to research on service models by extending space and functional roles of multiplex to the whole subsidiary facilities including movie screens.

• International Journal of Contents
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• v.9 no.1
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• pp.90-97
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• 2013

The L.A. riots, which happened during three days from April 29 to May 1, 1992, are viewed as the most deadly and destructive riots in American history. Depicted in blaring front-page headlines and violent pictures on television, this urban upheaval received epic exposure in many countries. In Korea, it was especially shocking due to the viewpoint that highlighted the conflict between Korean and African Americans. This paper aims to review the black-Korean conflict during the 1992 L.A. riots in a Korean movie, Western Avenue. It is a film that narrates the despair of Korean Americans in the context of the L.A. riots, while placing American ideologies on trial. It is the only feature-length film to portray the story of Korean Americans in the L.A. riots. This paper examines some of the factors that resulted from the 1992 L.A. riots before the discussion of Western Avenue. Then, the paper analyzes the story of the Korean American in the film, focusing on how this film deals with the black-Korean conflict during the 1992 L.A. riots.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• The Journal of Korean Academic Society of Nursing Education
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• v.24 no.4
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• pp.376-390
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• 2018

Purpose: The purpose of this study was to (a) synthesize nursing education literature using a smartphone for Korean nursing college students based on Whittemore and Knafl's integrative five-step review method and to (b) evaluate the quality appraisal of each article using Gough's weight of evidence. Methods: Articles published in Korea were identified through electronic search engines and scholarly websites using a combination of three search terms, including nursing student, smartphone, and education. Scientific, peer-reviewed articles in nursing education for Korean college nursing students, written in Korean or in English, and published between January 2000 and May 2018 were included in this review. Thirteen papers met the inclusion criteria and had above average ratings in quality appraisals. Results: Three characteristics related to nursing education using a smartphone were derived: (a) as a familiar media, motivating learning and enabling self-directed learning, (b) for the purpose of education or evaluation utilizing the educational movie of application, and (c) the iterative exercise of smartphone usage reinforces student learning. Conclusion: Smartphone use is an effective tool for improving nursing knowledge and skills for nursing college students in nursing education. Future research is needed to standardize smartphone applications across schools for nursing education.

• Korean Management Science Review
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• v.32 no.1
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• pp.133-145
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• 2015

Motion picture industry in Korea has been growing constantly and aroused various kinds of research attention. Particularly, the introduction of official box-office database service brought quantitative studies. However, approaches based on diffusion models have been rarely found with domestic film markets. In addition to the fundamental statistical review on Korea and US film markets, we applied a diffusion model to daily box-office revenue. Unlike conventional preference of Gamma distribution on the film markets, estimation results proved that BMIC can also explain the trend of daily revenue successfully. The comparison with BMIC showed that there is a distinctive difference in diffusion patterns of Korea and US film markets. Generally, word-of-mouth effect appeared more significant in Korea.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.101-106
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• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.