Difference between revisions of "R: tidytext RPJP BAPPENAS"
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Onnowpurbo (talk | contribs) |
Onnowpurbo (talk | contribs) |
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# create corpus from pdfs | # create corpus from pdfs | ||
| − | + | docs <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF)) | |
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| + | # docs <- VCorpus(DirSource("data", recursive=TRUE)) | ||
| + | # Get the document term matrices | ||
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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", | ||
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inspect(dtm_unigram) | inspect(dtm_unigram) | ||
inspect(dtm_bigram) | inspect(dtm_bigram) | ||
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| + | converted %>% | ||
| + | tidy() %>% | ||
| + | filter(!grepl("[0-9]+", term)) | ||
| + | # converted adalah DocumentTermMatrix | ||
Revision as of 12:44, 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