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Movie Analytics

A Hollywood Introduction to Big Data

  • Book
  • © 2015

Overview

Authors:
  1. Dominique Haughton

    1. Bentley University, Waltham, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Mark-David McLaughlin

    1. Information and Process Management, Bentley University, Waltham, USA

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    You can also search for this author in PubMed   Google Scholar

  3. Kevin Mentzer

    1. Business, Bentley University, Waltham, USA

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  4. Changan Zhang

    1. Business, Bentley University, Waltham, USA

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  • 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

Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST, volume 0)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-viii
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  2. What Do We Know About Analyzing Movie Data?

    • Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 1-2

  3. What Does “Big Data” Mean? The Data Scientist Point of View

    • Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 3-4

  4. Visualization of Very Large Networks: The Co-starring Social Network

    • Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 5-24

  5. Movie Attendance and Trends

    • Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 25-36

  6. Oscar Prediction and Prediction Markets

    • Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 37-39

  7. Can We Predict Oscars from Twitter and Movie Review Data?

    • Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
    Pages 41-54

  8. Back Matter

    Pages 55-64
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Keywords

About this book

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.

Reviews

“It covers various approaches and techniques for investigating big data on example of information available from the huge movie industry. … This innovative monograph can serve to lecturers and researchers interested in trying new approaches from movie evaluations in their own studies related to big data, networks, text mining, and complex systems.” (Stan Lipovetsky, Technometrics, Vol. 59 (2), April, 2017)

“This book covers the methods of analyzing big data in order to determine the success of a motion picture, what revenue it will bring in, and even how long it will last at a given location. … it will be most enjoyed by professional statisticians engaged in success prediction.” (James Van Speybroeck, Computing Reviews, January, 2016)

Authors and Affiliations

  • Bentley University, Waltham, USA

    Dominique Haughton

  • Information and Process Management, Bentley University, Waltham, USA

    Mark-David McLaughlin

  • Business, Bentley University, Waltham, USA

    Kevin Mentzer, Changan Zhang

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