Chapter 7. Text Classification

In this chapter, we will cover the following recipes:

• Bag of words feature extraction
• Training a Naive Bayes classifier
• Training a decision tree classifier
• Training a maximum entropy classifier
• Training scikit-learn classifiers
• Measuring precision and recall of a classifier
• Calculating high information words
• Combining classifiers with voting
• Classifying with multiple binary classifiers
• Training a classifier with NLTK-Trainer

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...