With the rise of social media, understanding a person’s sentiment, their mood and anxiety levels, has become just as important to law enforcement agencies as identifying online anonymous blog posts that aim to radicalize, promote terrorism, or criminal activity. Many of these mood and identity techniques rely on basic statistical correlations, word counts, collocated word groups, or keyword density. We claim that an alternative technique that uses word semantics reflecting personality or characteristics of self can provide a more accurate profile of a person. To test this we analyse Shakespeare’s Sonnets, a collection of poems believed to contain a Dark Lady and two other ‘voices’. We use an exploratory combinatorial data analysis technique called seriation in combination with RPAS, a multi-faceted text analysis approach that draws on a writer’s personality, or self, to visualize the 154 sonnets and to investigate whether it is possible to identify subtle characteristics within a person’s writing style from small texts when an author’s identity is known. We find that RPAS, not only clusters the Dark Lady and other sonnets, but also has the potential to discriminate subtle shifts in personality from texts as small as 90 words.