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

Korea Information Processing Society (한국정보처리학회)
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Competition Analysis to Improve the Performance of Movie Box-Office Prediction

영화 매출 예측 성능 향상을 위한 경쟁 분석

  • 하귀갑 (숭실대학교 컴퓨터학과) ;
  • 이수원 (숭실대학교 소프트웨어학부)
  • Received : 2017.06.29
  • Accepted : 2017.08.18
  • Published : 2017.09.30
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Abstract

Although many studies tried to predict movie revenues in the last decade, the main focus is still to learn an efficient forecast model to fit the box-office revenues. However, the previous works lack the analysis about why the prediction errors occur, and no method is proposed to reduce the errors. In this paper, we consider the prediction error comes from the competition between the movies that are released in the same period. Our purpose is to analyze the competition value for a movie and to predict how much it will be affected by other competitors so as to improve the performance of movie box-office prediction. In order to predict the competition value, firstly, we classify its sign (positive/negative) and compute the probability of positive sign and the probability of negative sign. Secondly, we forecast the competition value by regression under the condition that its sign is positive and negative respectively. And finally, we calculate the expectation of competition value based on the probabilities and values. With the predicted competition, we can adjust the primal predicted box-office. Our experimental results show that predictive competition can help improve the performance of the forecast.

영화 매출에 대한 연구가 많이 있었지만 공통적인 핵심주제는 영화 매출에 대한 효율적인 예측모델을 훈련하는 것이다. 그러나 과거의 연구에서는 예측 오차를 발생시키는 요인에 대한 분석이 부족하여 이러한 오차를 줄이는 방법에 대한 연구가 이루어지지 않았다. 본 연구에서는 같은 시기에 개봉되고 있는 영화들 간의 영향이 예측 오차에 대한 주요인이라는 가정하에 한 영화가 다른 경쟁영화에서 영향을 받는 정도(경쟁값)를 분석하여 영화매출예측 성능을 향상시키는 것을 목표로 한다. 경쟁값을 예측하기 위하여, 먼저 경쟁값의 극성(양수/음수)에 대해 분류하고 양수의 확률과 음수의 확률을 계산한 다음 회귀분석을 이용하여 양수인 값과 음수인 값을 예측한다. 마지막으로, 확률값과 예측값을 통하여 경쟁값의 기댓값을 계산하여 초기 예측된 매출을 보정한다. 실험 결과에 의하면 제안 방법을 통하여 영화 매출 예측의 정확도가 향상됨을 알 수 있었다.

Keywords

References

  1. G. He and S. Lee, "Multi-model or Single Model? A Study of Movie Box-Office Revenue Prediction." in Proceedings of the IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp.321-325, 2015.
  2. J. S. Simonoff and I. R. Sparrow, "Predicting movie grosses: winners and losers, blockbusters and sleepers," Chance, Vol. 13, No.3, pp.15-24, 2000.
  3. A. D. Vany and W. D. Walls, "Uncertainty in the movie industry: does star power reduce the terror of the box office," Journal of Cultural Economics, Vol.23, No.4, pp.285-318, 1999. https://doi.org/10.1023/A:1007608125988
  4. R. Sharda and D. Delen, "Predicting box-office success of motion pictures with neural networks," Expert Systems with Applications, Vol.30, pp.243-254, 2006. https://doi.org/10.1016/j.eswa.2005.07.018
  5. S. Asur and B. A. Huberman, "Predicting the Future with Social Media," 2010, http://www.arxiv.orgarXiv:1003.5699v1.
  6. Y. Liu, "Word of mouth for movies: its dynamics and impact on box office revenue," Journal of Marketing, Vol.70, pp.74-89, Jul., 2006. https://doi.org/10.1509/jmkg.70.3.74
  7. W. Duan, B. Gu, and A. B. Whinston, "The dynamics of online word-of-mouth and product sales: an empirical investigation of the movie industry," Journal of Retailing, Vol.84, No.2, pp.233-242, 2008. https://doi.org/10.1016/j.jretai.2008.04.005
  8. W. Zhang and S. Skiena, "Improving movie gross prediction through news analysis," in Proceedings of the International Conference on Web Intelligence, pp.301-304, 2009.
  9. Y. Liu, X. Huang, A. An, and X. Yu, "ARSA: A sentimentaware model for predicting sales performance using blogs," in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.607-614, 2007.
  10. M. Joshi, D. Das, K. Gimpel, and N. A. Smith, "Movie reviews and revenues: an experiment in text regression," Human Language Technologies: in Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, pp.293-296, 2010.
  11. X. Yu, Y. Liu, J. X. Huang, and A. An, "Mining online reviews for predicting sales performance: a case study in the movie domain," IEEE Transactions on Knowledge and Data Engineering, Vol.24, No.4, pp.720-734, Apr., 2012. https://doi.org/10.1109/TKDE.2010.269
  12. W. Ding, Y. Shang, L. Guo, X. Hu, R. Yan, and T. He, "Video popularity prediction by sentiment propagation via implicit network," in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp.1621-1630, 2015.
  13. T. Kim, J. Hong, and P. Kang, "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Vol.31, No.2, pp.364-390, 2015. https://doi.org/10.1016/j.ijforecast.2014.05.006
  14. A. Bhave, H. Kulkarni, V. Biramane, and P. Kosamkar, "Role of different factors in predicting movie success," in Proceedings of the International Conference on Pervasive Computing (ICPC), pp.1-4, 2015.
  15. A. Esuli and F. Sebastiani, "SENTIWORDNET: A highcoverage lexical resource for opinion mining," Evaluation, 2007:1-26.
  16. A. Pak and P. Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining," in Proceedings of the International Conference on Language Resources and Evaluation, Vol.10, 2010.