Improving Movie Gross Prediction through News Analysis

—Traditional movie gross predictions are based on numerical and categorical movie data. But since the 1990s, text sources such as news have been proven to carry extra and meaningful information beyond traditional quantitative finance data, and thus can be used as predictive indicators in finance. In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help us to predict movie grosses. By analyzing two different models (regression and k-nearest neighbor models), we find models using only news data can achieve similar performance to those use numerical and categorical data from The Internet Movie Database (IMDB). Moreover, we can achieve better performance by using the combination of IMDB data and news data. Further, the improvement is statistically significant.

• Type: Conference
• Year: 2009
• Where: WEBI
• Authors: Wenbin Zhang, Steven Skiena
• Keywords: Categorical Movie Data, Internet Technology, Movie Gross Predictions, Quantitative finance Data, WEBI 2009