Hands-On Machine Learning for Algorithmic Trading

Sentiment analysis with pretrained word vectors

In Chapter 15, Word Embeddings, we discussed how to learn domain-specific word embeddings. Word2vec, and related learning algorithms, produce high-quality word vectors, but require large datasets. Hence, it is common that research groups share word vectors trained on large datasets, similar to the weights for pretrained deep learning models that we encountered in the section on transfer learning in the previous chapter.

We are now going to illustrate how to use pretrained Global Vectors for Word Representation (GloVe) provided by the Stanford NLP group with the IMDB review dataset (see GitHub for references and the sentiment_analysis_pretrained_embeddings notebook for implementation details). ...