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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 developmen...

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Detalles Bibliográficos
Autores principales: Haughton, Dominique, McLaughlin, Mark-David, Mentzer, Kevin, Zhang, Changan
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-09426-7
http://cds.cern.ch/record/2112882
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author Haughton, Dominique
McLaughlin, Mark-David
Mentzer, Kevin
Zhang, Changan
author_facet Haughton, Dominique
McLaughlin, Mark-David
Mentzer, Kevin
Zhang, Changan
author_sort Haughton, Dominique
collection CERN
description 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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spelling cern-21128822021-04-21T20:00:43Zdoi:10.1007/978-3-319-09426-7http://cds.cern.ch/record/2112882engHaughton, DominiqueMcLaughlin, Mark-DavidMentzer, KevinZhang, ChanganMovie analytics: a hollywood introduction to big dataMathematical Physics and MathematicsMovies 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.Springeroai:cds.cern.ch:21128822015
spellingShingle Mathematical Physics and Mathematics
Haughton, Dominique
McLaughlin, Mark-David
Mentzer, Kevin
Zhang, Changan
Movie analytics: a hollywood introduction to big data
title Movie analytics: a hollywood introduction to big data
title_full Movie analytics: a hollywood introduction to big data
title_fullStr Movie analytics: a hollywood introduction to big data
title_full_unstemmed Movie analytics: a hollywood introduction to big data
title_short Movie analytics: a hollywood introduction to big data
title_sort movie analytics: a hollywood introduction to big data
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-09426-7
http://cds.cern.ch/record/2112882
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