In the last chapter, we discussed an important NLP task called sentiment analysis. In that chapter, you used a data set of video game reviews and trained a model to predict whether a review carried a negative or positive sentiment by analyzing the text. You learned about various preprocessing steps that you can perform to improve the quality of the text, such as removing stop words and lemmatizing (i.e., converting words to a base form; e.g., plural to singular). You used a special type of model known as long short-term memory (LSTM). LSTM models can process sequences such as sentences and learn the relationships and dependencies in them to produce an outcome. LSTM models do this by maintaining a state (or memory) containing information about the past, as it processes a sequence one element at a time. The LSTM model can use the memory of past inputs it has seen along with the current input to produce an output at any given time.