Find common words

Say you want to visualize common words across multiple documents. You can do this with commonality.cloud().

Each of our coffee and chardonnay corpora is composed of many individual tweets. To treat the coffee tweets as a single document and likewise for chardonnay, you paste() together all the tweets in each corpus along with the parameter collapse = " ". This collapses all tweets (separated by a space) into a single vector. Then you can create a single vector containing the two collapsed documents.

a_single_string <- paste(a_character_vector, collapse = " ")

Once you're done with these steps, you can take the same approach you've seen before to create a VCorpus() based on a VectorSource from the all_tweets object.

This exercise is part of the course

Text Mining with Bag-of-Words in R

View Course

Exercise instructions

Create all_coffee by using paste() with collapse = " " on coffee_tweets$text.
Create all_chardonnay by using paste() with collapse = " " on chardonnay_tweets$text.
Create all_tweets using c() to combine all_coffee and all_chardonnay. Make all_coffee the first term.
Convert all_tweets using VectorSource().
Create all_corpus by using VCorpus() on all_tweets.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create all_coffee
___ <- ___(___, ___)

# Create all_chardonnay
___ <- ___(___, ___)

# Create all_tweets
___ <- ___(___, ___)

# Convert to a vector source
___ <- ___(___)

# Create all_corpus
___ <- ___(___)

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