Predicting movie box-office revenues using deep neural networks

In the film industry, the ability to predict a movie's box-office revenues before its theatrical release can decrease its financial risk. However, accurate predictions are not easily obtained. The complex relationship between movie-related data and movie box-office revenues, plus the increasing volume of data in online movie databases, pose challenges for their effective analysis. In this paper, a multimodal deep neural network, incorporating input about movie poster features learned in a data-driven fashion, is proposed for movie box-office revenues prediction. A convolutional neural network (CNN) is built to extract features from movie posters. By pre-training the CNN, features that are relevant to movie box-office revenues can be learned. To evaluate the performance of the proposed multimodal deep neural network, comparative studies with other prediction techniques were carried out on an Internet Movie Database dataset, and visualization of movie poster features was also performed. Experimental results demonstrate the superiority of the proposed multimodal deep neural network for movie box-office revenues prediction.