  Writing and reading are dynamic processes. As an author composes a text, a
sequence of words is produced. This sequence is one that, the author hopes,
causes a revisitation of certain thoughts and ideas in others. These processes
of composition and revisitation by readers are ordered in time. This means that
text itself can be investigated under the lens of dynamical systems. A common
technique for analyzing the behavior of dynamical systems, known as recurrence
quantification analysis (RQA), can be used as a method for analyzing sequential
structure of text. RQA treats text as a sequential measurement, much like a
time series, and can thus be seen as a kind of dynamic natural language
processing (NLP). The extension has several benefits. Because it is part of a
suite of time series analysis tools, many measures can be extracted in one
common framework. Secondly, the measures have a close relationship with some
commonly used measures from natural language processing. Finally, using
recurrence analysis offers an opportunity expand analysis of text by developing
theoretical descriptions derived from complex dynamic systems. We showcase an
example analysis on 8,000 texts from the Gutenberg Project, compare it to
well-known NLP approaches, and describe an R package (crqanlp) that can be used
in conjunction with R library crqa.
