Getting past single words

Text Mining with Bag-of-Words in R

Ted Kwartler

Instructor

Unigrams, bigrams, trigrams, oh my!

# Use only first 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…

# Make a unigram DTM on first 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!

# Load RWeka package
library(RWeka)

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

# Make a 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)

Let's practice!

Text Mining with Bag-of-Words in R