  The distances between words calculated in word units are studied and compared
with the distributions of the Random Matrix Theory (RMT). It is found that the
distribution of distance between the same words can be well described by the
single-parameter Brody distribution. Using the Brody distribution fit, we found
that the distance between given words in a set of texts can show mixed
dynamics, coexisting regular and chaotic regimes. It is found that
distributions correctly fitted by the Brody distribution with a certain
goodness of the fit threshold can be identifid as stop words, usually
considered as the uninformative part of the text. By applying various threshold
values for the goodness of fit, we can extract uninformative words from the
texts under analysis to the desired extent. On this basis we formulate a fully
agnostic recipe that can be used in the creation of a customized set of stop
words for texts in any language based on words.
