Movie Analytics: A Hollywood Introduction to Big Data (SpringerBriefs in Statistics)

Haughton, Dominique; McLaughlin, Mark-David; Mentzer, Kevin; Zhang, Changan

ISBN 10: 3319094254 / ISBN 13: 9783319094250

Published by Springer, 2015

About this Item

Seller Inventory # EBBV.3319094254.N

Bibliographic Details

• Title: Movie Analytics: A Hollywood Introduction to Big Data (SpringerBriefs in Statistics)
• Publisher: Springer
• Publication Date: 2015
• Language: English
• ISBN 10: 3319094254
• ISBN 13: 9783319094250
• Binding: Soft cover
• Condition: new

About this title

Synopsis

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.

Review

“It covers various approaches and techniques for investigating big data on example of information available from the huge movie industry. ... This innovative monograph can serve to lecturers and researchers interested in trying new approaches from movie evaluations in their own studies related to big data, networks, text mining, and complex systems.” (Stan Lipovetsky, Technometrics, Vol. 59 (2), April, 2017)

“This book covers the methods of analyzing big data in order to determine the success of a motion picture, what revenue it will bring in, and even how long it will last at a given location. ... it will be most enjoyed by professional statisticians engaged in success prediction.” (James Van Speybroeck, Computing Reviews, January, 2016)