Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Lydia: a system for large-scale news analysis
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
Access: news and blog analysis for the social sciences
Proceedings of the 19th international conference on World wide web
Movie reviews and revenues: an experiment in text regression
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Prediction of movies box office performance using social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Pre-release box-office success prediction for motion pictures
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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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.