In textual analysis, many corpora include texts which have a chronological
order. The temporal evolution of (key) words is relevant in order to highlight the
distinctive features of the chronological corpus. In a typical bag-of-words approach
data are organized in word-type x time-point contingency tables. Such discrete data
can be thought of as continuous objects represented by functional relationships. The
aims of this study are identifying a specific sequential pattern for each word as a
functional object, and determining prototype patterns representing clusters of words
portraying a similar evolution. We propose the application of a flexible waveletbased
model for curve clustering to a corpus of end-of-year addresses delivered by
the ten Presidents of Italian Republic in the period 1949-2011.
