Hands-On Machine Learning for Algorithmic Trading

The BoW model

The BoW 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.

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, is a very useful starting point.

The following diagram (the one on the right) illustrates how this document model converts text data into a matrix with numerical entries, where each row corresponds to a document and each column to a ...