By Dominique Haughton, Mark-David McLaughlin, Kevin Mentzer, Changan Zhang
Movies is absolutely not an identical once you methods to learn motion picture information, together with key information mining, textual content mining and social community analytics recommendations. those suggestions could then be utilized in never-ending different contexts. within the motion picture software, this subject opens a full of life dialogue at the present advancements in great facts from a knowledge technology point of view. This ebook is geared to utilized researchers and practitioners and is intended to be useful. The reader will take a hands-on procedure, operating textual content mining and social community analyses with software program applications lined within the ebook. those contain R, SAS, Knime, Pajek and Gephi. The nitty-gritty of ways to construct datasets wanted for many of the analyses should be mentioned in addition. This comprises tips to extract appropriate Twitter info and create a co-starring community from the IMDB database given reminiscence constraints. The authors additionally consultant the reader via an research of motion picture attendance information through a practical dataset from France.
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Extra resources for Movie Analytics: A Hollywood Introduction to Big Data
For a given movie theater, the rows correspond to the weeks and the columns to the years. csv was thus stored in the element called “A” of the list). files[ind], 1, nchar(all. frame': 53 obs. frame': 53 obs. $ 2011: int [1:53] 288 302 372 173 705 594 963 283 404 490 … 2 Basic Description of the Data The number of years and weeks available for each movie theater can be checked by applying the functions ncol and nrow on each element of the list with lapply. This provides 53 weeks for all movie theaters (with the first and/or the last week usually not complete weeks) and from 3 to 17 years of data, depending on the movie theater according to Table 1.
3 © The Author(s) 2015 D. 1007/978-3-319-09426-7 55 56 Appendix: Code Needed to Perform the Analyses in Chap. 3 Appendix: Code Needed to Perform the Analyses in Chap.
17, the 1-, 2- and 3-cores are delimited by blue, green and red boundaries respectively, and the colors represent the shells (blue for the 1-shell, green for the 2-shell, red for the 3-shell). Figure 18a gives another example of the decomposition into k-shells of a simple network due to De Nooy et al. (2011), where it is clear that a k-core can be disconnected, even if the initial graph is connected. Figure 18b gives the LaNet-vi2 rendition of the network in Fig. 18a. 8 K-Core Representations of the IMDb Co-starring Network Using LaNet-vi2 LaNet-vi2 is a “Large Network” Visualization tool created by collaboration between the University of Indiana, Université Paris-Sud, and the Centre National pour la Recherche Scientifique in France (CNRS, National Center for Scientific Research).