The Document Term Matrix is one of the most common data storage formats for text based data.

The DTM is based on the bag-of-words model. Each word is a feature in the data set. This leads to sparse matricies.

Some of the most popular R libraries and Python packages use DTM.

• R: tm, quanteda, CRAN Task View: NLP
• Python: NLTK, Gensim, Spacy

• each row represents one document (such as a book or article)
• each column represents one term
• each value (typically) contains the number of appearances of that term in that document

tidytext to DTM

 > tidy() # to tidy format 
 > cast() # to DTM
 

5.3.1 Example: mining financial articles does not run!