Bag-of-Words

Introduction

The bag-of-words (BoW) is an essential technique to represent text data in a numerical format that machine learning algorithms can understand. We normally use this technique when we’ve cleaned the text data and need to use it for machine-learning model training. It allows us to treat text data as an unordered collection of words and disregard grammar, word order, and context. As a result, we find its application in scenarios where the context or sequence of words is less important than the frequency of individual words.

Calculating BoW

Let’s consider a simple BoW calculation for a given document. Suppose we have the following document A: “I love to eat cakes. Cakes are delicious.” To perform a BoW calculation: