Movie Analytics - A Hollywood Introduction to Big Data

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.

Sommario

What do we know about analyzing movie data: section on past literature.- What does "Big Data" mean; the data scientist point of view.- Visualization of very large networks: the co-starring social network.- Movie attendance and trends.- Oscar prediction and prediction markets.- Can we predict Oscars from Twitter and movie review data.

Relazione

"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)

Dettagli sul prodotto

• Autori: Dominiqu Haughton, Dominique Haughton, Mark-Davi McLaughlin, Mark-David McLaughlin, Mentzer, Kevin Mentzer, Kevin D. Mentzer, Nathalie Villa-Vialaneix, Changan Zhang
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 27.06.2014
• EAN: 9783319094250
• ISBN: 978-3-31-909425-0
• Pagine: 64
• Dimensioni: 157 mm x 235 mm x 5 mm
• Peso: 138 g
• Illustrazioni: VIII, 64 p. 53 illus., 45 illus. in color.
• Serie: SpringerBriefs in Statistics; SpringerBriefs in Statistics
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP; Scienze sociali, diritto, economia > Sociologia > Metodi di ricerca sociale empirica e qualitativa; C, Data Mining, Statistics, Grafikprogrammierung, Wissensbasierte Systeme, Expertensysteme, Mathematics and Statistics, Computer Graphics, Data Mining and Knowledge Discovery, Statistics for Social Sciences, Humanities, Law, Expert systems / knowledge-based systems, Graphics programming