Distributed Text Mining in R

Stefan Theußl, Ingo Feinerer, Kurt Hornik

Publication: Working/Discussion PaperWU Working Paper

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R has recently 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) an increase of the amount of data to be analyzed leads to increasing computational workload. Fortunately, adequate parallel programming models like MapReduce and the corresponding open source implementation called Hadoop allow for processing data sets beyond what would fit into memory. In this paper we present the package "tm.plugin.dc" offering a seamless integration between "tm" and Hadoop. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.
Original languageEnglish
Publication statusPublished - 1 Mar 2011

Publication series

SeriesResearch Report Series / Department of Statistics and Mathematics

WU Working Paper Series

  • Research Report Series / Department of Statistics and Mathematics

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