In this chapter, we covered common NLP tasks, such as preprocessing and exploratory analysis of text using the NLTK library. The unstructured characteristics of real-world data need extensive preprocessing, such as tokenization, stemming, and stop word removal, to make it suitable for ML. As you saw in the examples, NLTK provides a very extensive API for carrying out these preprocessing steps. It provides built-in packages and modules, and supports flexibility to build custom modules, such as user-defined stemmers and tokenizers.

We also discussed using NLTK for POS tagging, which is another common NLP task, used for issues such as word sense disambiguation and answering questions. Applications such as sentiment classification are widely used for their research and business value. We covered some basic examples of text classification, in the context of sentiment analysis, for tweets and movie reviews, using the NLTK corpora and sklearn. While these can be used in simple NLP applications, more complex text classification, using deep learning, will be explained in subsequent chapters.