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Unofficial fork of tm package for R with support for parallel operations using plyr
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Parallelization w/ plyr

This fork provides some additional multicore support by favoring plyr::llply over base::lapply for tm_map.VCorpus. Passing tm_map any of plyr's options for llply is supported.

Benchmark for removeWords on a 10,000 document corpus with and without parallelization (2011 iMac 2.8 GHz Intel Core i5, 8 GB RAM, OS X 10.6.7, R version 2.13.0 Patched (2011-04-23 r55622), with 4 parallel workers). (Not necessarily representative of anything.)


system.time(tm::tm_map(inboundCorpus[1:10000], removeWords, myStopWords))
elapsed = 100.681  

With llply:

system.time(tmparallel::tm_map(inboundCorpus[1:10000], removeWords, myStopWords, .parallel=T, .progress='text'))  
elapsed = 53.809  

With equivalent size snow MPI cluster:

makeCluster(4, type="MPI")  
system.time(tm::tm_map(inboundCorpus[1:10000], removeWords, myStopWords))
elapsed = 117.673

Option to use Rstem instead of SnowballStemmer

This adds an option to stemDocument to specify stemmer="Rstem" to use the implementation from's Rstem package. This eliminates nasty dependencies on Rjava, etc. and has some performance advantages.

Benchmark for stemDocument on a 48,415 document corpus with Rstem vs. Snowball stemmer and with Rstem and parallelization (2011 iMac 2.8 GHz Intel Core i5, 8 GB RAM, OS X 10.6.7, R version 2.13.0 Patched (2011-04-23 r55622), with 4 parallel workers). (Not necessarily representative of anything.)

With SnowballStemmer, non parallel:

system.time(tm_map(inboundCorpus, stemDocument, .progress='text'))
   user  system elapsed 
731.575   4.220 730.456

With Rstem, non parallel:

system.time(tm_map(inboundCorpus, stemDocument, "english", stemmer="Rstem", .progress='text'))
    user  system elapsed 
 180.282   0.626 181.013 

With Rstem, parallel:

system.time(tm_map(inboundCorpus, stemDocument, "english", stemmer="Rstem", .progress='text', .parallel=T))
   user  system elapsed 
240.981   3.216 152.029 


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