Verder dan losse woorden

Text mining met bag-of-words in R

Ted Kwartler

Instructor

Unigrams, bigrams, trigrams, oh my!

# Gebruik alleen de eerste 2 coffee-tweets
tweets$text[1:2]

[1] @ayyytylerb that is so true drink lots of coffee
[2] RT @bryzy_brib: Senior March tmw morning at 7:25 A.M. in the SENIOR lot. Get up early, make yo coffee/breakfast, cus this will only happen…

# Maak een unigram-DTM op de eerste 2 coffee-tweets
unigram_dtm <- DocumentTermMatrix(text_corp)
unigram_dtm

<<DocumentTermMatrix (documents: 2, terms: 18)>>
Non-/sparse entries: 18/18
Sparsity           : 50%
Maximal term length: 15
Weighting          : term frequency (tf)

Unigrams, bigrams, trigrams, oh my!

# Laad het RWeka-pakket
library(RWeka)

# Definieer bigram-tokenizer
tokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))

# Maak een bigram-TDM 
bigram_tdm <- TermDocumentMatrix(clean_corpus(text_corp),
                                 control = list(tokenize = tokenizer))
bigram_tdm

<<DocumentTermMatrix (documents: 2, terms: 21)>>
Non-/sparse entries: 21/21
Sparsity           : 50%
Maximal term length: 19
Weighting          : term frequency (tf)

Laten we oefenen!

Text mining met bag-of-words in R