Different frequency criteria

Text Mining with Bag-of-Words in R

Ted Kwartler

Instructor

Term weights

Default term frequency = simple word count
Frequent words can mask insights
Adjust term weighting via TfIdf
Words appearing in many documents are penalized

Term weights

# Standard term weighting
tf_tdm <- TermDocumentMatrix(text_corp)
tf_tdm_m <- as.matrix(tf_dtm)
tf_tdm_m[505:510, 5:10]

# TfIdf weighting
tf_idf_tdm <- TermDocumentMatrix(text_corp, 
    control = list(weighting = weightTfIdf))
tf_idf_tdm_m <- as.matrix(tf_idf_dtm)
tf_tdm_m <- as.matrix(tf_dtm)

Retaining document metadata

# Ensure the first 2 columns are doc_id & text
names(tweets)[1:2] <- c('doc_id','text')

# Create VCorpus including metadata
test_corpus <- VCorpus(DataframeSource(tweets))

# Clean and view results
text_corpus <- clean_corpus(text_corpus)
content(text_corpus[[1]])

$content
[1] "ayyytylerb true drink lots coffee"

meta(text_corpus[[1]])

$meta
  id      : 1
  author  : thejennagibson
  date    : 8/9/2013 2:43
  language: en

Let's practice!

Text Mining with Bag-of-Words in R