Introduction

Text classification is a way to categorize documents or pieces of text. By examining the word usage in a piece of text, classifiers can decide what class label to assign to it. A binary classifier decides between two labels, such as positive or negative. The text can either be one label or another, but not both, whereas a multi-label classifier can assign one or more labels to a piece of text.

Classification works by learning from labeled feature sets, or training data, to later classify an unlabeled feature set. A labeled feature set is simply a tuple that looks like (feat, label), while an unlabeled feature set is a feat by itself. A feature set is basically a key-value mapping of feature names to feature values. In the case of text classification, the feature names are usually words, and the values are all True. As the documents may have unknown words, and the number of possible words may be very large, words that don't occur in the text are omitted, instead of including...