Extracting the frequency of terms using the Bag of Words model

One of the main goals of text analysis with the Bag of Words model is to convert text into a numerical 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 numerical representation.

Machine learning algorithms need numerical data to work with so that they can analyze the data and extract meaningful information. This is where the Bag of Words model comes in. This model extracts 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...