Difference between revisions of "R: tidytext RPJP BAPPENAS"
Jump to navigation
Jump to search
Onnowpurbo (talk | contribs) |
Onnowpurbo (talk | contribs) |
||
Line 15: | Line 15: | ||
+ | |||
+ | 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) | ||
− | |||
− | |||
− | |||
− | |||
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)