Movie Box office Prediction via Joint Actor Representations and Social Media Sentiment

TL;DR

This paper introduces a novel deep learning model that combines film metadata, social media sentiment, and actor social network features to improve box office prediction accuracy, outperforming existing models by 14%.

Contribution

It proposes a FC-GRU-CNN model integrating multiple data sources and social network analysis for more accurate and interpretable box office predictions.

Findings

Model achieves 14% higher accuracy than C-LSTM.

Incorporates actor social network and sentiment data.

Uses long-term memory and feature mapping for improved prediction.

Abstract

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.