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Text Mining in R



install.packages( c("tm", "VCorpus", "hunspell"),  dependencies = TRUE)

install.packages( c("Rweka", "RMySQL", "textmining"),  dependencies = TRUE)

install.packages( c("iemisctext", "ngram"), dependencies = TRUE)

if(TRUE)
{
  WORKDIR="D:/DeltaPartners2017/FieldOperations/TextMining/Mining/";
  setwd(WORKDIR);
  Sys.setlocale("LC_ALL","Arabic")
  memory.limit(size = 16072)
 
 
  library(tm);
  library("iemisctext")
  library(ngram)
  library(RWeka)
 
  data(anarchy)
  a <- anarchy="" documenttermmatrix="" p="">
  findFreqTerms(a, 5)
 
  data("crude")
 
  # Corpust --> DataFrame
  ddataframeCrude<-data .frame="" content="" stringsasfactors="F)</p" text="unlist(sapply(crude,">  dataframeCrude[1,]
 
}
 
 
  # Raw data to Corpus

   serv_log <- nbsp="" p="" paste="" read.csv="" sep="" test.csv="" workdir="">  mycorpus <- corpus="" ectorsource="" p="" serv_log=""> 
  mydfCorpus <- data.frame="" stringsasfactors="FALSE)</p" text="unlist(mycorpus)">  mydfCorpus[1]
 
  # Map the corpus by applying differnt filters

   mycorpusAfterMap <- mycorpus="" p="" tm_map="" tolower="">  mycorpusAfterMap <- mycorpusaftermap="" p="" removepunctuation="" tm_map="">  mycorpusAfterMap <- mycorpusaftermap="" p="" removenumbers="" tm_map="">  mycorpusAfterMap <- kind="en" mycorpusaftermap="" p="" removewords="" stopwords="" tm_map="">  mycorpusAfterMap <- mycorpusaftermap="" p="" stripwhitespace="" tm_map="">  mycorpusAfterMap <- mycorpusaftermap="" p="" stemdocument="" tm_map=""> 
  # If apply this, gives error#  mycorpusAfterMap <- laintextdocument="" mycorpusaftermap="" p="" tm_map=""> 
  ########################  Generate DT matrix    #################################
  
myDTmatrix <- documenttermmatrix="" mycorpusaftermap="" p="">  findFreqTerms( myDTmatrix, 5)
  keyword.freq <- appearing="" as.matrix="" frequency="" mydtmatrix="" of="" p="" rowsums="" words=""> 
  myDTmatrix_NonSparse <- 0.9="" mydtmatrix="" p="" removesparseterms="">  keyword_NonSparse.freq <- appearing="" as.matrix="" frequency="" mydtmatrix_nonsparse="" of="" p="" rowsums="" words=""> 
 
  # Find N-gram from corpus using NGRAM package
  
  strCorpus <- 1="" concatenate="" lapply="" mycorpusaftermap="" p="">  bigram <- n="2)</p" ngram="" strcorpus="">  # print(ngram(strCorpus, sep=" " , n=2), output="full")
  get.phrasetable(bigram)
 
  # Find N-gram from corpus using RWeka package
  #mydfCorpus <- data.frame="" stringsasfactors="FALSE)</p" text="unlist(mycorpusAfterMap)">  #bigram <- max="2))</p" min="2," mydfcorpus="" ngramtokenizer="" weka_control="">  

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