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  1. The English word “raccoon” derives from an Algonquin word meaning, “scratches with his hands!”↩︎

  2. On the other hand, the more biased stop word list may be helpful when modeling a corpus with gender imbalance, depending on your goal; words like “she” and “her” can identify where women are mentioned.↩︎

  3. This simple, “weak” stemmer is handy to have in your toolkit for many applications. Notice how we implement it here using dplyr::case_when().↩︎

  4. Part-of-speech information is also sometimes used directly in machine learning↩︎

  5. Google has since worked to correct this problem.↩︎

  6. The random forest implementation in the ranger package, demonstrated in Section @ref{comparerf}, does not handle special characters in columns names well.↩︎

  7. In other situations you may do best using a different architecture, for example, when working with dense, tabular data.↩︎