Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Stefan Theußl, Ingo Feinerer, Kurt Hornik
Title: A tm Plug-In for Distributed Text Mining in R
Abstract: R has gained explicit text mining support with the tm package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text) corpora. However, we typically face two challenges when analyzing large corpora: (1) the amount of data to be processed in a single machine is usually limited by the available main memory (i.e., RAM), and (2) the more data to be analyzed the higher the need for efficient procedures for calculating valuable results. Fortunately, adequate programming models like MapReduce facilitate parallelization of text mining tasks and allow for processing data sets beyond what would fit into memory by using a distributed file system possibly spanning over several machines, e.g., in a cluster of workstations. In this paper we present a plug-in package to tm called tm.plugin.dc implementing a distributed corpus class which can take advantage of the Hadoop MapReduce library for large scale text mining tasks. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.

Page views:: 10103. Submitted: 2011-03-16. Published: 2012-11-13.
Paper: A tm Plug-In for Distributed Text Mining in R     Download PDF (Downloads: 12002)
tm.plugin.dc_0.2-4.tar.gz: R source package Download (Downloads: 463; 7KB)
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v51i05-data.tar.bz2: data set Download (Downloads: 553; 209MB)

DOI: 10.18637/jss.v051.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.