  The availability of quantitative text analysis methods has provided new ways
of analyzing literature in a manner that was not available in the
pre-information era. Here we apply comprehensive machine learning analysis to
the work of William Shakespeare. The analysis shows clear changes in the style
of writing over time, with the most significant changes in the sentence length,
frequency of adjectives and adverbs, and the sentiments expressed in the text.
Applying machine learning to make a stylometric prediction of the year of the
play shows a Pearson correlation of 0.71 between the actual and predicted year,
indicating that Shakespeare's writing style as reflected by the quantitative
measurements changed over time. Additionally, it shows that the stylometrics of
some of the plays is more similar to plays written either before or after the
year they were written. For instance, Romeo and Juliet is dated 1596, but is
more similar in stylometrics to plays written by Shakespeare after 1600. The
source code for the analysis is available for free download.
