• 한국전자통신학회논문지
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• 제14권2호
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• pp.377-388
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• 2019

본고에서 제안된 시스템은 제4차 산업혁명의 인공지능 시대에 맞춰 작동하는 빅데이터 시스템으로 제안되었다. 제안된 시스템은 정부의 새로운 지능형 빅데이터 정보시스템 개발 측면에서 하나의 좋은 예가 될 수 있다. 예를 들면 기존 영화관입장권통합전산망의 연계 혹은 그 기능 그대로 부처의 시스템으로 도입될 수도 있다. 제안된 시스템은 이를 위해 유저의 프로파일을 영화제작자 등의 사업자에게 전송하는데 여기에는 비교데이터로서 제공된다. 곧 유저별 특성데이터로 정보가 전송되며 이른바 '새로운 재해석'내용까지 포함한 실제 유저가 느끼는 영화품평을 통해 제작자는 개봉된 영화의 작품성, 흥행성, 손익분기점의 3가지 요소의 성공가능성을 실시간으로 가늠할 수 있다.

• 한국컴퓨터정보학회논문지
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• 제27권3호
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• pp.201-207
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• 2022

본 논문에서는 영화 추천 시 평점뿐 아니라 사용자 리뷰도 함께 사용하는 영화 추천 모형을 제안한다. 제안 모형은 고객의 선호도를 다기준 관점에서 이해하기 위해, 사용자 리뷰에 속성기반 감성분석을 적용하도록 설계되었다. 이를 위해, 제안 모형은 고객이 남긴 리뷰를 다기준 속성별로 나누어 암시적 속성을 파악하고, BERT를 통해 이를 감성 분석함으로써 각 사용자가 중요시 생각하는 속성을 선별적으로 협업필터링에 결합하여 추천 결과를 생성한다. 본 연구에서는 유용성을 검증하기 위해 제안모형을 실제 영화 추천 사례에 적용해 보았다. 실험결과 전통적인 협업필터링 보다 제안 모형의 추천 정확도가 향상되는 것을 확인할 수 있었다. 본 연구는 개인의 특성을 고려하여 모형을 선별하여 사용하는 새로운 접근법을 제시하였고, 속성 각각에 대한 평가 없이 리뷰로부터 여러 속성을 파악할 수 있는 방법을 제시했다는 측면에서 학술적, 실무적 의의가 있다.

• 한국IT서비스학회지
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• 제17권1호
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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.

• 한국시스템다이내믹스연구
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• 제10권2호
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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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• 제9권1호
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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.

• 한국IT서비스학회지
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• 제21권2호
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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.

• 한국간호교육학회지
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• 제24권4호
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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.

• 경영과학
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• 제32권1호
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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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• 제23권8호
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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.

• 융합정보논문지
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• 제10권10호
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• pp.40-46
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• 2020

인터넷의 발전으로 사용자들은 자신의 경험이나 의견을 공유한다. 영화평과 같은 비정형 문서의 전체적인 감정이나 장르 등의 정보를 고려하지 않고 연관된 키워드를 사용하기 때문에 적절한 감정 상황에 따른 감성 정확도를 저해한다. 따라서 사용자들이 작성한 비정형 문서가 속한 장르나 전반적인 감정 등의 정보를 기반으로 감성을 예측하는 시스템을 제안한다. 먼저, 비정형 문서로부터 기쁨, 화남, 공포, 슬픔 등의 감정 집합과 연관된 대표 키워드를 추출하고, 감정 특징단어들의 정규화된 가중치와 비정형 문서의 정보를 훈련 집합으로 CNN과 LSTM을 조합한 시스템에 훈련한다. 최종적으로 영화 정보와 형태소 분석기와 n-gram을 통해 추출한 정제된 단어들과 이모티콘, 이모지 등을 테스트함으로써 감정을 이용한 감성 예측 정확도와 F-measure 측면에서 향상됨을 보였다. 제안한 예측시스템은 슬픈 영화에서 슬픈 단어의 사용과 공포 영화에서 무서운 단어 등의 사용으로 인해 부정으로 판단하는 오류를 피함으로써, 감성을 상황에 따라 적절하게 예측할 수 있다.