Recurrent neural networks are useful for solving problems where data involves sequences. Some examples of applications involving sequences are seen in text classification, time series prediction, the sequence of frames in videos, DNA sequences, and speech recognition.

In this chapter, we will develop a sentiment (positive or negative) classification model using a recurrent neural network. We will begin by preparing the data for developing the text classification model, followed by developing a sequential model, compiling the model, fitting the model, evaluating the model, prediction, and model performance assessment using a confusion matrix. We will also review some tips for sentiment classification performance optimization.

More specifically, in this chapter, we will cover the following topics:

• Preparing data for model building
• Developing a recurrent neural network model
• Fitting the model
• Model evaluation and prediction
• Performance optimization tips and best practices