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      트위터를 이용한 기계학습 기반의 영화흥행 예측 = Data Engineering : Predicting Movie Success based on Machine Learning Using Twitter

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      https://www.riss.kr/link?id=A100083748

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      다국어 초록 (Multilingual Abstract)

      This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people``s perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.
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      This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short c...

      This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people``s perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

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      참고문헌 (Reference)

      1 김은미, "한국 영화의 흥행 결정 요인에 관한 연구" 한국언론학회 47 (47): 190-220, 2003

      2 김연형, "영화 흥행 결정 요인과 흥행 성과 예측 연구" 한국통계학회 18 (18): 859-869, 2011

      3 Y. Liu, "Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue" 70 (70): 74-89, 2006

      4 Statistic Brain, "Twitter Statistics"

      5 J. Bollen, "Twitter Mood Predicts the Stock Market" 2 (2): 1-8, 2011

      6 Twitter, "The Streaming APIs|Twitter Developers"

      7 N. Terry, "The Determinants of Foreign Box Office Revenue for English Language Movies" 2 (2): 1-12, 2010

      8 박선영, "SNS를 통한 구전 효과가 영화 흥행에 미치는 영향 -<써니>의 사례를 중심으로-" 한국콘텐츠학회 12 (12): 40-53, 2012

      9 L. Barbosa, "Robust Sentiment Detection on Twitter from Biased and Noisy Data" 36-44, 2010

      10 E. T. K. Sang, "Predicting the 2011 Dutch Senate Election Results with Twitter" 53-60, 2011

      1 김은미, "한국 영화의 흥행 결정 요인에 관한 연구" 한국언론학회 47 (47): 190-220, 2003

      2 김연형, "영화 흥행 결정 요인과 흥행 성과 예측 연구" 한국통계학회 18 (18): 859-869, 2011

      3 Y. Liu, "Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue" 70 (70): 74-89, 2006

      4 Statistic Brain, "Twitter Statistics"

      5 J. Bollen, "Twitter Mood Predicts the Stock Market" 2 (2): 1-8, 2011

      6 Twitter, "The Streaming APIs|Twitter Developers"

      7 N. Terry, "The Determinants of Foreign Box Office Revenue for English Language Movies" 2 (2): 1-12, 2010

      8 박선영, "SNS를 통한 구전 효과가 영화 흥행에 미치는 영향 -<써니>의 사례를 중심으로-" 한국콘텐츠학회 12 (12): 40-53, 2012

      9 L. Barbosa, "Robust Sentiment Detection on Twitter from Biased and Noisy Data" 36-44, 2010

      10 E. T. K. Sang, "Predicting the 2011 Dutch Senate Election Results with Twitter" 53-60, 2011

      11 G. Lee, "Predicting Financial Success of a Movie Using Bayesian Choice Model" 1428-1433, 2006

      12 A. Tumasjan, "Predicting Election with Twitter: What 140 Characters Reveal about Political Sentiment" 178-185, 2010

      13 NAVER, "Naver Movie"

      14 S. Albert, "Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry" 22 (22): 249-270, 1998

      15 Korean Film Council, "KOFIC Cinema Tickets Integrated Computer Network"

      16 I. H. Witten, "Data Mining: Practical Machine Learning Tools and Techniques" Morgan Kaufmann Series in Data Management Systems 85-99, 2011

      17 J. Yim, "An Analysis of Correlation between Movie Attendance and Related Tweets for Predicting Box Office" 20 (20): 2013

      18 M. Pennacchiotti, "A Machine Learning Approach to Twitter User Classification" 281-288, 2011

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2012-10-31 학술지명변경 한글명 : 소프트웨어 및 데이터 공학 -> 정보처리학회논문지. 소프트웨어 및 데이터 공학 KCI등재
      2012-10-10 학술지명변경 한글명 : 정보처리학회논문지B -> 소프트웨어 및 데이터 공학
      외국어명 : The KIPS Transactions : Part B -> KIPS Transactions on Software and Data Engineering
      KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2003-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2002-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2000-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.35 0.35 0.28
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.23 0.19 0.511 0.06
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