Textual data is a very large source of information, and properly handling it is crucial to success. So, to handle this textual data, we need to follow some basic text processing steps.
Most of the processing steps covered in this section are commonly used in NLP and involve combining a number of steps into one executable flow. This is what we refer to as the NLP pipeline. This flow can be a combination of tokenization, stemming, word frequency, parts of speech tagging, and many more elements.
Let's look into the details on how to implement the steps in the NLP pipeline and, specifically, what each stage of processing does. We will use the Natural Language Toolkit (NLTK) package—an NLP toolkit written in Python, which you can install with the following:
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger...
