In the bag-of-words model, we create from a document a bag containing words found in the document. In this model, we don't care about the word order. For each word in the document, we count the number of occurrences. With these word counts, we can do statistical analysis, for instance, to identify spam in e-mail messages.

If we have a group of documents, we can view each unique word in the corpus as a feature; here, "feature" means parameter or variable. Using all the word counts, we can build a feature vector for each document; "vector" is used here in the mathematical sense. If a word is present in the corpus but not in the document, the value of this feature will be 0. Surprisingly, NLTK doesn't have a handy utility currently to create a feature vector. However, the machine learning Python library, scikit-learn, does have a CountVectorizer class that we can use. In the next chapter, Chapter 10, Predictive Analytics and Machine Learning, we will do more with scikit...