  Most of the data produced in software projects is of textual nature: source
code, specifications, or documentations. The advances in quantitative analysis
methods drove a lot of data analytics in software engineering. This has
overshadowed to some degree the importance of texts and their qualitative
analysis. Such analysis has, however, merits for researchers and practitioners
as well.
  In this chapter, we describe the basics of analysing text in software
projects. We first describe how to manually analyse and code textual data.
Next, we give an overview of mixed methods to automatic text analysis including
N-Grams and clone detection as well as more sophisticated natural language
processing identifying syntax and contexts of words. Those methods and tools
are of critical importance to aid in the challenges in today's huge amounts of
textual data.
  We illustrate the introduced methods via a running example and conclude by
presenting two industrial studies.
