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Movie Analytics:A Hollywood Introduction to Big Data

Mark-David McLaughlin - Personal Name; Dominique Haughton - Personal Name; Kevin Mentzer - Personal Name; Changan Zhang - Personal Name;

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.


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Detail Information
Series Title
SpringerBriefs in Statistics
Call Number
-
Publisher
: ., 2015
Collation
VIII, 64
Language
English
ISBN/ISSN
978-3-319-09425-0
Classification
NONE
Content Type
-
Media Type
-
Carrier Type
-
Edition
1
Subject(s)
Computer Graphics
Law
Education
Humanities
Statistics in Social Sciences
Behavorial Sciences
Public Policy
Data Mining and Knowledge Discovery
Specific Detail Info
-
Statement of Responsibility
Dominique Haughton , Mark-David McLaughlin , Kevin Mentzer , Changan Zhang
Other Information
Cataloger
Fandi
Source
-
Other version/related

No other version available

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  • Movie Analytics
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