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 / Votes
"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)
Series
Edition
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
8 s/w Abbildungen, 45 farbige Abbildungen
VIII, 64 p. 53 illus., 45 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 5 mm
Weight
ISBN-13
978-3-319-09425-0 (9783319094250)
DOI
10.1007/978-3-319-09426-7
Schweitzer Classification
Dominique Haughton (Ph.D. MIT 1983) is Professor of Mathematical Sciences at Bentley University in Waltham, Massachusetts, near Boston, and Affiliated Researcher at the Université Toulouse 1, France. Her major areas of interest are applied statistics, statistics and marketing, the analysis of living standards surveys, data mining, and model selection. She is the editor-in-chief of Case Studies in Business, Industry and Government Statistics (CSBIGS), and has published over fifty articles in scholarly journals, including The American Statistician, Annals of Statistics, Sankhya, Communications in Statistics, and Statistica Sinica. Dominique is a Fellow of the American Statistical Association.
Jonathan Haughton (Ph.D. Harvard 1983) is Professor of Economics at Suffolk University, and Senior Economist at the Beacon Hill Institute for Public Policy, both in Boston. A specialist in the areas of economic development, international trade, and taxation, and a prize-winning teacher, he has lectured, taught, or conducted research in over a score of countries on five continents. His Handbook on Poverty and Inequality (with Shahidur Khandker) was published by the World Bank in 2009, his articles have appeared in over 30 scholarly journals, and he has written numerous book chapters and over a hundred reports.
