Haughton, D., McLaughlin, M.-D., Mentzer, K.D., Zhang, C.
2015, X, 100 p. 25 illus.
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Current introduction to big data issues through an appealing and surprisingly complex subject: movie analytics
Delves into text mining techniques through movie reviews, twitter data and social network analysis
Includes visualization of the co-starring network, prediction of Oscar winners and analysis of movie attendance data
All methods may be applied to myriad other contexts
Movies will never be the same after you learn how to analyze movie data, including key data mining, text mining and social network analytics concepts. These techniques may then be used in endless other contexts. In the movie application, this topic opens a lively discussion on the current developments in big data from a data science perspective. This book is geared to applied researchers and practitioners and is meant to be practical. The reader will take a hands-on approach, running text mining and social network analyses with software packages covered in the book. These include R, SAS, Knime, Pajek and Gephi. The nitty-gritty of how to build datasets needed for the various analyses will be discussed as well. This includes how to extract suitable Twitter data and create a co-starring network from the IMDB database given memory constraints. The authors also guide the reader through an analysis of movie attendance data via a realistic dataset from France.
Content Level »Research
Keywords »Big Data - Data Mining - Internet movie DataBase (IMDB) - Movie Analytics - Oscar prediction with data - Text Mining
What do we know about analyzing movie data: section on past literature.- What is meant by “big data”; the data scientist point of view.- Visualization of very large networks: the co-starring social network.- Text Mining: twitter movie related data.- Text Mining: movie review data.- Dollars and cents: analysis of financial data (revenue, budget etc.).- Analysis of movie attendance data.- Gold standard: Intradate data and Oscars.- Can one predict Oscars from twitter and review text data.