Abstract:  Film box office is usually affected by many factors. However， the word-of-mouth， as a key factor， is often neglected by traditional prediction models. In order to improve the prediction accuracy， a novel box office prediction model based on the sentiment analysis， the entropy weight method and the LSTM neural network is presented in this paper. Firstly， the input index system is constructed by selecting eight influence factors， namely， word-of-mouth， box office of the previous day， box office of the same day last week， ticket price， service fee， screening rate， holiday or not， and search index. Secondly， the method of sentiment analysis is used to analyze the text of film review， and the sentiment index is used to quantify the word-of-mouth factor. Then， in order to quantify the impact of different factors on the box office， the entropy weight method is used to assign weights to different factors. Finally， the Long Short-Term Memory neural network is applied to predict the box office. Simulation results indicate that the prediction accuracy of the presented SA-EW-LSTM model reaches 94.9% and 94.8% on two different data sets， respectively， which is obviously superior to the other five classical models， and the effectiveness of the presented model is verified.

Key words:  , sentiment analysis, entropy weight method, LSTM, box office prediction