  In this technical note we suggest a novel approach to discover temporal
(related and unrelated to language dilation) and personality (authorship
attribution) in historical datasets. We exemplify our approach on the State of
the Union speeches given by the past 42 US presidents: this dataset is known
for its relatively small amount of data, and high variability of the amount and
style of texts. Nevertheless we manage to achieve about 95\% accuracy on the
authorship attribution task, and pin down the date of writing to a single
presidential term.
