RNNs for text data
RNNs are commonly applied to various natural language processing tasks, from machine translation to sentiment analysis, that we already encountered in Part 3 of this book. In this section, we will illustrate how to apply an RNN to text data to detect positive or negative sentiment (easily extensible to a finer-grained sentiment scale) and to predict stock returns.
More specifically, we'll use word embeddings to represent the tokens in the documents. We covered word embeddings in Chapter 16, Word Embeddings for Earnings Calls and SEC Filings. They are an excellent technique for converting a token into a dense, real-value vector because the relative location of words in the embedding space encodes useful semantic aspects of how they are used in the training documents.
We saw in the previous stacked RNN example that TensorFlow has a built-in embedding layer that allows us to train vector representations specific to the task at hand. Alternatively, we...
