Introduction

In the previous chapter, we learned about the concepts of Natural Language Processing (NLP) and text analytics. We also took a quick look at various preprocessing steps. In this chapter, we will learn how to make text understandable to machine learning algorithms.

As we know, to use a machine learning algorithm on textual data, we need a numerical or vector representation of text data since most of these algorithms are unable to work directly with plain text or strings. But before converting the text data into numerical form, we will need to pass it through some preprocessing steps such as tokenization, stemming, lemmatization, and stop-word removal.

So, in this chapter, we will learn a little bit more about these preprocessing steps and how to extract features from the preprocessed text and convert them into vectors. We will also explore two popular methods for feature extraction (Bag of Words and Term Frequency-Inverse Document Frequency), as well as various methods...