Practical Natural Language Processing

Chapter 4. Text Classification

Organizing is what you do before you do something, so that when you do it, it is not all mixed up.

A.A. Milne

All of us check email every day, possibly multiple times. A useful feature of most email service providers is the ability to automatically segregate spam emails away from regular emails. This is a use case of a popular NLP task known as text classification, which is the focus of this chapter. Text classification is the task of assigning one or more categories to a given piece of text from a larger set of possible categories. In the email spam–identifier example, we have two categories—spam and non-spam—and each incoming email is assigned to one of these categories. This task of categorizing texts based on some properties has a wide range of applications across diverse domains, such as social media, e-commerce, healthcare, law, and marketing, to name a few. Even though the purpose and application of text classification may vary from domain to domain, the underlying abstract problem remains the same. This invariance of the core problem and its applications in a myriad of domains makes text classification by far the most widely used NLP task in industry and the most researched in academia. In this chapter, we’ll discuss the usefulness of text classification and how to build text classifiers for our use cases, along with some practical tips for real-world scenarios.

In machine learning, classification is the problem of categorizing a data instance ...