Text feature engineering
In preceding sections, we've discussed some of the methods by which we might take a dataset and extract a subset of valuable features. These methods have broad applicability but are less helpful when dealing with non-numerical/non-categorical data, or data that cannot be easily translated into numerical or categorical data. In particular, we need to apply different techniques when working with text data.
The techniques that we'll study in this section fall into two main categories—cleaning techniques and feature preparation techniques. These are typically implemented in roughly that order and we'll study them accordingly.
Cleaning text data
When we work with natural text data, a different set of approaches apply. This is because in real-world contexts, the idea of a naturally clean text dataset is pretty unsafe; text data is rife with misspellings, non-dictionary constructs like emoticons, and in some cases, HTML tagging. As such, we need to be...
