The 20 newsgroups dataset is a fairly well-known dataset among the NLP community. It is near-ideal for demonstration purposes. This dataset has a near-uniform distribution across 20 classes. This uniform distribution makes iterating rapidly on classification and clustering techniques easy.

We will use the famous 20 newsgroups dataset for our demonstrations as well:

from sklearn.datasets import fetch_20newsgroups  # import packages which help us download dataset 
twenty_train = fetch_20newsgroups(subset='train', shuffle=True, download_if_missing=True)
twenty_test = fetch_20newsgroups(subset='test', shuffle=True, download_if_missing=True)

Most modern NLP methods rely heavily on machine learning methods. These methods need words that are written as strings of text to be converted into a numerical representation. This numerical representation can be as simple as assigning a unique integer ID to slightly more comprehensive vector of float values. In the case of the latter, this is sometimes referred to as vectorization.