KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Predicting Movie Success based on Machine Learning Using Twitter

트위터를 이용한 기계학습 기반의 영화흥행 예측

  • 임준엽 (가톨릭대학교 컴퓨터공학과) ;
  • 황병연 (가톨릭대학교 컴퓨터정보공학부)
  • Received : 2014.02.13
  • Accepted : 2014.05.26
  • Published : 2014.07.31
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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.

본 논문에서는 영화의 흥행을 예측하기 위한 방법을 제안한다. 최근 영화시장이 성장함에 따라 시장의 수요를 예측하기 위한 다양한 연구들이 수행되고 있다. 영화는 비교적 수명주기가 짧은 문화상품이다. 따라서 안정적인 수익을 창출하기 위해 개봉 전 마케팅비용 및 개봉 후 스크린 수 등에 대한 설계가 필요하다. 이를 위해서는 상품의 수요와 경제적인 수익규모에 대한 계산이 선행되어야 한다. 기존 관련 연구들의 경우 예측을 위한 변수로서 주로 영화 자체의 속성들이나 시장에서의 경쟁요인 등을 이용한다. 그러나 정작 상품을 구매하는 주체인 잠재관객들에 대한 비중은 비교적 미비하다. 따라서 본 논문에서는 사람들이 가진 영화에 대한 인지도를 고려하기 위해 트위터를 하나의 설문표본으로서 활용했다. 기존에 사용된 변수들과 트위터에서 추출한 정보를 오프라인 요소와 온라인 요소로 정의하고, 두 요소를 취합하여 기계학습을 적용했다. 실험을 통해 본 논문에서 제시하는 예측기법을 검증했으며, 실험결과 약 95%의 정확도로 영화의 흥행을 예측했다.

Keywords

References

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