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 signifficant size. (authors' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3974 |
| Date | 11 1900 |
| Creators | TheuĂl, Stefan, Feinerer, Ingo, Hornik, Kurt |
| Publisher | University of California, Los Angeles |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf, application/x-gzip |
| Relation | http://www.jstatsoft.org/v51/i05, http://epub.wu.ac.at/3974/ |
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