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S. Theußl, I. Feinerer, K. Hornik:
"A tm Plug-In for Distributed Text Mining in R";
Journal of Statistical Software, 51 (2012), 5; S. 1 - 31.

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 e cient 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 t into memory by using a distributed le 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 e ciently handle data sets of signi cant size.