Movie Box office Prediction via Joint Actor Representations and Social Media Sentiment
This work addresses the problem of improving box office forecasting for the film industry, particularly in Asian markets, by incorporating previously underutilized social network data, though it is incremental in nature.
The paper tackled movie box office prediction by integrating film metadata, social media sentiment, and actors' social network features, achieving a 14% higher accuracy than the previous best model.
In recent years, driven by the Asian film industry, such as China and India, the global box office has maintained a steady growth trend. Previous studies have rarely used long-term, full-sample film data in analysis, lack of research on actors' social networks. Existing film box office prediction algorithms only use film meta-data, lack of using social network characteristics and the model is less interpretable. I propose a FC-GRU-CNN binary classification model in of box office prediction task, combining five characteristics, including the film meta-data, Sina Weibo text sentiment, actors' social network measurement, all pairs shortest path and actors' art contribution. Exploiting long-term memory ability of GRU layer in long sequences and the mapping ability of CNN layer in retrieving all pairs shortest path matrix features, proposed model is 14% higher in accuracy than the current best C-LSTM model.