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＜電子ブック＞
Movie Analytics : A Hollywood Introduction to Big Data / by Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
(SpringerBriefs in Statistics. ISSN:21915458 ; 0)

版	1st ed. 2015.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2015
本文言語	英語
大きさ	VIII, 64 p. 53 illus., 45 illus. in color : online resource
著者標目	*Haughton, Dominique author McLaughlin, Mark-David author Mentzer, Kevin author Zhang, Changan author SpringerLink (Online service)
件　名	LCSH:Social sciences -- Statistical methods  全ての件名で検索 LCSH:Data mining LCSH:Computer graphics FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Data Mining and Knowledge Discovery FREE:Computer Graphics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-3-319-09426-7

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