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