Our goal for analyzing the news articles is to generate a vector space model of the collection of documents. This attempts to pull the salient features for the documents into a vector of floating-point numbers. Features can be words or information from the documents' metadata encoded for the vector. The feature values can be 0 or 1 for presence, an integer for raw frequency, or the frequency scaled in some form.

In our case, we'll use the feature vector to represent a selection of the tokens in a document. Often, we can use all the tokens, or all the tokens that occur more than once or twice. However, in this case, we don't have a lot of data, so we'll need to be more selective in the features that we include. We'll consider how we select these in a few sections.

For the feature values, we'll use a scaled version of the token frequency called term frequency-inverse document frequency (tf-idf). There are good libraries for this, but this is a basic metric in working with...