When it comes to dealing with text documents that consist of millions of words, converting them into numerical representations is necessary. The reason for this is to make them usable for machine learning algorithms. These algorithms need numerical data so that they can analyze them and output meaningful information. This is where the bag-of-words approach comes into the picture. This is basically a model that learns a vocabulary from all of the words in all the documents. It models each document by building a histogram of all of the words in the document.
Building a bag-of-words model
Getting ready
In this recipe, we will build a bag-of-words model to extract a document term matrix, using the sklearn.feature_extraction.text...
