TY - GEN
T1 - Improving movie gross prediction through news analysis
AU - Zhang, Wenbin
AU - Skiena, Steven
PY - 2009
Y1 - 2009
N2 - Traditional movie gross predictions are based on numerical and categorical movie data from The Internet Movie Database (IMDB). In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help people 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 using IMDB data. Moreover, we can achieve better performance by using the combination of IMDB data and news data. Further, the improvement is statistically significant.
AB - Traditional movie gross predictions are based on numerical and categorical movie data from The Internet Movie Database (IMDB). In this paper, we use the quantitative news data generated by Lydia, our system for large-scale news analysis, to help people 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 using IMDB data. Moreover, we can achieve better performance by using the combination of IMDB data and news data. Further, the improvement is statistically significant.
UR - https://www.scopus.com/pages/publications/84863116500
U2 - 10.1109/WI-IAT.2009.53
DO - 10.1109/WI-IAT.2009.53
M3 - Conference contribution
AN - SCOPUS:84863116500
SN - 9780769538013
T3 - Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
SP - 301
EP - 304
BT - Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
T2 - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
Y2 - 15 September 2009 through 18 September 2009
ER -