The Journal of the Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회논문지)

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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An Experimental Evaluation of Box office Revenue Prediction through Social Bigdata Analysis and Machine Learning

소셜 빅데이터 분석과 기계학습을 이용한 영화흥행예측 기법의 실험적 평가

  • 장재영 (한성대학교 컴퓨터공학과)
  • Received : 2017.03.21
  • Accepted : 2017.06.09
  • Published : 2017.06.30
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Abstract

With increased interest in the fourth industrial revolution represented by artificial intelligence, it has been very active to utilize bigdata and machine learning techniques in almost areas of society. Also, such activities have been realized by development of forecasting systems in various applications. Especially in the movie industry, there have been numerous attempts to predict whether they would be success or not. In the past, most of studies considered only the static factors in the process of prediction, but recently, several efforts are tried to utilize realtime social bigdata produced in SNS. In this paper, we propose the prediction technique utilizing various feedback information such as news articles, blogs and reviews as well as static factors of movies. Additionally, we also experimentally evaluate whether the proposed technique could precisely forecast their revenue targeting on the relatively successful movies.

인공지능으로 대표되는 4차 산업혁명에 대한 관심이 증가함에 따라 사회 전반에 빅데이터 및 머신러닝 활용하려는 움직임이 활발해지고 있다. 이러한 움직임은 다양한 분야에서의 예측 시스템 개발로 현실화되고 있다. 특히 영화 산업에서는 투자, 마케팅 등에 활용을 위해 흥행 여부를 사전에 예측하고자하는 여러 가지 시도가 있어왔다. 예전에는 영화에 대한 정적 데이터만을 고려한 예측이 주류를 이뤘으나, 최근에는 실시간으로 생성되는 소셜 데이터를 활용하여 예측하고자하는 노력이 진행되고 있다. 본 논문에서는 영화의 정적 데이터와 더불어 기사, 블로그, 영화평 등 다양한 피드백 정보를 활용한 예측 기법을 제안한다. 또한 제안한 기법을 활용하여 상대적으로 흥행에 성공한 영화만을 대상으로 이들의 흥행정도를 정량적으로 추정할 수 있는지의 여부를 실험적으로 평가하였다.

Keywords

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