One of the main goals of text analysis is to convert text into numeric form so that we can use machine learning on it. Let's consider text documents that contain many millions of words. In order to analyze these documents, we need to extract the text and convert it into a form of numeric representation.

Machine learning algorithms need numeric data to work with so that they can analyze the data and extract meaningful information. This is where the Bag of Words model comes into picture. This model extracts a vocabulary from all the words in the documents and builds a model using a document term matrix. This allows us to represent every document as a bag of words. We just keep track of word counts and disregard the grammatical details and the word order.

Let's see what a document-term matrix is all about. A document term matrix is basically a table that gives us counts of various words that occur in the document. So a text document...