Movie Analytics

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

   PDF

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.

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

“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)

• 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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