rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Movie Analytics
    sub title: A Hollywood Introduction to Big Data
  name:
    name part: Haughton, Dominique.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: McLaughlin, Mark-David.
    role:
      role term: author.
  name:
    name part: Mentzer, Kevin.
    role:
      role term: author.
  name:
    name part: Zhang, Changan.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VIII, 64 p. 53 illus., 45 illus. in color. online resource.
  abstract: 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.
  table of contents: What do we know about analyzing movie data: section on past literature.- What does "Big Data" mean; the data scientist point of view.- Visualization of very large networks: the co-starring social network.- Movie attendance and trends -- Oscar prediction and prediction markets -- Can we predict Oscars from Twitter and movie review data.
  note: by Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang.
  subject:
    topic: Statistics
  subject:
    topic: Data mining
  subject:
    topic: Computer graphics
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computer Graphics
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Statistics
  identifier: 9783319094267
  identifier: http://dx.doi.org/10.1007/978-3-319-09426-7
  record info:
    record creation date: 151005
    record change date: 20151005160957.0
    record identifier: 978-3-319-09426-7