R: tidytext RPJP BAPPENAS: Difference between revisions

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Line 6: Line 6:
   
   
  # create corpus from pdfs
  # create corpus from pdfs
  converted <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF)) %>%
  docs <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF))
  DocumentTermMatrix()
converted %>%
  tidy() %>%
  filter(!grepl("[0-9]+", term))
# converted adalah DocumentTermMatrix


# docs <- VCorpus(DirSource("data", recursive=TRUE))
# Get the document term matrices


docs <- VCorpus(DirSource("data", recursive=TRUE))
# Get the document term matrices
  BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
  BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
  dtm_unigram <- DocumentTermMatrix(docs, control = list(tokenize="words",  
  dtm_unigram <- DocumentTermMatrix(docs, control = list(tokenize="words",  
Line 30: Line 23:
  inspect(dtm_unigram)
  inspect(dtm_unigram)
  inspect(dtm_bigram)
  inspect(dtm_bigram)
converted %>%
  tidy() %>%
  filter(!grepl("[0-9]+", term))
# converted adalah DocumentTermMatrix





Revision as of 05:44, 6 November 2018

library(tidyverse)
library(tidytext)
library(tm)
directory <- "data-pdf"

# create corpus from pdfs
docs <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF))
# docs <- VCorpus(DirSource("data", recursive=TRUE))
# Get the document term matrices
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
dtm_unigram <- DocumentTermMatrix(docs, control = list(tokenize="words", 
    removePunctuation = TRUE, 
    stopwords = stopwords("english"), 
    stemming = TRUE))
dtm_bigram <- DocumentTermMatrix(docs, control = list(tokenize = BigramTokenizer,
    removePunctuation = TRUE,
    stopwords = stopwords("english"),
    stemming = TRUE))
inspect(dtm_unigram)
inspect(dtm_bigram)


converted %>%
  tidy() %>%
  filter(!grepl("[0-9]+", term))
# converted adalah DocumentTermMatrix



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