Hands-On Machine Learning for Algorithmic Trading

Topic Modeling

In the last chapter, we converted unstructured text data into a numerical format using the bag-of-words model. This model abstracts from word order and represents documents as word vectors, where each entry represents the relevance of a token to the document.

The resulting document-term matrix (DTM), (you may also come across the transposed term-document matrix) is useful to compare documents to each other or to a query vector based on their token content, and quickly find a needle in a haystack or classify documents accordingly.

However, this document model is both high-dimensional and very sparse. As a result, it does little to summarize the content or get closer to understanding what it is about. In this chapter, we will ...