Chapter 7. Unstructured Data
In the previous chapter, we looked at different ways of building and fitting models on structured data. Unfortunately, these otherwise extremely useful methods are of no use (yet) when dealing with, for example, a pile of PDF documents. Hence, the following pages will focus on methods to deal with non-tabular data, such as:
- Extracting metrics from a collection of text documents
- Filtering and parsing natural language texts (NLP)
- Visualizing unstructured data in a structured way
Text mining is the process of analyzing natural language text; in most cases from online content, such as emails and social media streams (Twitter or Facebook). In this chapter, we are going to cover the most used methods of the tm package—although, there is a variety of further types of unstructured data, such as text, image, audio, video, non-digital contents, and so on, which we cannot discuss for the time being.
