Movie Analytics

A Hollywood Introduction to Big Data

  • Dominique Haughton
  • Mark-David McLaughlin
  • Kevin Mentzer
  • Changan Zhang

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 1-2
  3. Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 3-4
  4. Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 5-24
  5. Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 25-36
  6. Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 37-39
  7. Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 41-54
  8. Back Matter
    Pages 55-64

About this book

Introduction

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.

Keywords

Big Data Data Mining Internet Movie DataBase (IMDb) Movie Analytics Oscar prediction with data Python and Gephi R Text Mining Text Mining with SAS

Authors and affiliations

  • Dominique Haughton
    • 1
  • Mark-David McLaughlin
    • 2
  • Kevin Mentzer
    • 3
  • Changan Zhang
    • 4
  1. 1.Bentley UniversityWalthamUSA
  2. 2.Information and Process ManagementBentley UniversityWalthamUSA
  3. 3.BusinessBentley UniversityWalthamUSA
  4. 4.BusinessBentley UniversityWalthamUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-09426-7
  • Copyright Information The Author(s) 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-09425-0
  • Online ISBN 978-3-319-09426-7
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • About this book