In this chapter, we illustrated the use of the recurrent neural network model for text sentiment classification using IMDb movie review data. Compared to a regular densely connected network, recurrent neural networks are better suited to deal with data that has sequences in it. Text data is one such example that we worked with in this chapter.

In general, deep networks involve many factors or variables, and this calls for some amount of experimentation involving making changes to the levels for such factors before arriving at a useful model. In this chapter, we also developed five different movie review sentiment classification models. 

A variant of recurrent neural networks that has become popular is Long Short-Term Memory (LSTM) networks. LSTM networks are capable of learning long-term dependencies and help recurrent networks remember inputs for a longer time.

In the next chapter, we will go over an application example of using an LSTM network, where we will continue to use IMDb movie review data and explore further improvements that can be made to the sentiment classification model's performance.