Overview
- 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
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST, volume 0)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
Keywords
About this book
Reviews
“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)
Authors and Affiliations
Bibliographic Information
Book Title: Movie Analytics
Book Subtitle: A Hollywood Introduction to Big Data
Authors: Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-319-09426-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2015
Softcover ISBN: 978-3-319-09425-0Published: 12 October 2015
eBook ISBN: 978-3-319-09426-7Published: 05 October 2015
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: VIII, 64
Number of Illustrations: 8 b/w illustrations, 45 illustrations in colour
Topics: Statistics for Social Sciences, Humanities, Law, Data Mining and Knowledge Discovery, Computer Graphics