Hands-On Machine Learning for Algorithmic Trading

LSTM and word embeddings for sentiment classification

RNNs are commonly applied to various natural language processing tasks. We've already encountered sentiment analysis using text data in part three of this book.

We are now going to illustrate how to apply an RNN model to text data to detect positive or negative sentiment (which can easily be extended to a finer-grained sentiment scale).

We are going to use word embeddings to represent the tokens in the documents. We covered word embeddings in Chapter 15, Word Embeddings. They are an excellent technique to convert text into a continuous vector representation such that the relative location of words in the latent space encodes useful semantic aspects based on the words' usage in context. ...