I'm trying to predict an article's page views based on its content and a bunch of other factors. Everything except for the content is numeric (e.g. time of day, length of title, length of article).
I was reading about one-hot encoding and it seems I can treat the textual content of the article as a "bag of words." Then I can extract features like:
hasSpecificWord: 1 hasOtherWord: 0
Alternatively, I could use the counts of those words as a numeric feature.
Is there a standard way of approaching this problem in ML?