Difference between revisions of "R: tidytext RPJP BAPPENAS"
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| + | |||
| + | 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) | ||
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| − | |||
| − | |||
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Revision as of 12:41, 6 November 2018
library(tidyverse)
library(tidytext)
library(tm)
directory <- "data-pdf"
# create corpus from pdfs
converted <- 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
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)