Counting tokens – the document-term matrix
In this section, we first introduce how the bag-of-words model converts text data into a numeric vector space representations. The goal is to approximate document similarity by their distance in that space. We then proceed to illustrate how to create a document-term matrix using the sklearn library.
The bag-of-words model
The bag-of-words model represents a document based on the frequency of the terms or tokens it contains. Each document becomes a vector with one entry for each token in the vocabulary that reflects the token's relevance to the document.
Creating the document-term matrix
The document-term matrix is straightforward to compute given the vocabulary. However, it is also a crude simplification because it abstracts from word order and grammatical relationships. Nonetheless, it often achieves good results in text classification quickly and, thus, provides a very useful starting point.
The...
