A data science and machine learning approach to continuous analysis of Shakespeare's plays

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