Logo - springer
Slogan - springer

Statistics - Social Sciences & Law | Movie Analytics - A Hollywood Introduction to Big Data

Movie Analytics

A Hollywood Introduction to Big Data

Haughton, D., McLaughlin, M.-D., Mentzer, K.D., Zhang, C.

2015, X, 100 p. 25 illus.

Available Formats:

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.


ISBN 978-3-319-09426-7

digitally watermarked, no DRM

The eBook version of this title will be available soon

learn more about Springer eBooks

add to marked items


Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.


(net) price for USA

ISBN 978-3-319-09425-0

free shipping for individuals worldwide

Due: June 4, 2015

add to marked items

  • About this book

  • Current introduction to big data issues through an appealing and surprisingly complex subject: movie analytics
  • Delves into text mining techniques through movie reviews, twitter data and social network analysis
  • Includes visualization of the co-starring network, prediction of Oscar winners and analysis of movie attendance data
  • All methods may be applied to myriad other contexts
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.

Content Level » Research

Keywords » Big Data - Data Mining - Internet movie DataBase (IMDB) - Movie Analytics - Oscar prediction with data - Text Mining

Related subjects » Business, Economics & Finance - Database Management & Information Retrieval - Social Sciences & Law

Table of contents 

What do we know about analyzing movie data: section on past literature.- What is meant by “big data”; the data scientist point of view.- Visualization of very large networks: the co-starring social network.- Text Mining: twitter movie related data.- Text Mining: movie review data.- Dollars and cents: analysis of financial data (revenue, budget etc.).- Analysis of movie attendance data.- Gold standard: Intradate data and Oscars.- Can one predict Oscars from twitter and review text data.

Popular Content within this publication 



Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.