Summary
In this chapter, we discussed a new way of generating text features that use shallow neural networks for unsupervised machine learning. We saw how the resulting word embeddings capture interesting semantic aspects beyond the meaning of individual tokens by capturing some of the context in which they are used. We also covered how to evaluate the quality of word vectors using analogies and linear algebra.
We used Keras to build the network architecture that produces these features and applied the more performant Gensim implementation to financial news and SEC filings. Despite the relatively small datasets, the word2vec embeddings did capture meaningful relationships. We also demonstrated how appropriate labeling with stock price data can form the basis for supervised learning.
We applied the doc2vec algorithm, which produces a document rather than token vectors, to build a sentiment classifier based on Yelp business reviews. While this is unlikely to yield tradeable...
