{"id":8,"date":"2022-02-16T02:36:28","date_gmt":"2022-02-16T02:36:28","guid":{"rendered":"http:\/\/midterm.hannak.sites.carleton.edu\/blog\/?page_id=8"},"modified":"2022-02-17T02:08:01","modified_gmt":"2022-02-17T02:08:01","slug":"introduction","status":"publish","type":"page","link":"https:\/\/midterm.hannak.sites.carleton.edu\/blog\/introduction\/","title":{"rendered":"Midterm"},"content":{"rendered":"\n

INTRODUCTION<\/strong><\/p>\n\n\n\n

How can digital humanities help us consider the structure of the Shakespearean tragedy? This is the essential question at the core of this midterm project. The data analysis portion of this project uses five tragedies by the playwright, all in relatively modern English: Othello<\/a><\/em>, Hamlet<\/a><\/em>, King Lear<\/a><\/em>, Julius Caesar<\/a><\/em>, and The Tragedy of Titus Andronicus<\/a><\/em>. For comparison, some graphs also include the play Votes for Women<\/a><\/em> by Elizabeth Robins; this is a different play both by genre and by topic. If you want to think about this piece in scientific terms, perhaps we could say that it serves as a control<\/em> — that is, a baseline from which we can determine whether what we see as trends in these tragedies is really specific to Shakespearean tragedies or just a result of common language use. All plays are sourced from Project Gutenberg<\/a><\/em> (linked by name); you can access each individual script by clicking on its name. Data for this specific project was cleaned via two processes: 1) removal of legal information that is a part of the text documentation in Project Gutenberg work (legal restrictions still apply but could not be included in the data); 2) removal of all character names. To explain the latter, let’s take a look at how plays are formatted (taken from King Lear<\/em> as linked above):<\/p>\n\n\n\n

ACT I. Scene I.\n[King Lear's Palace.]\n\nEnter Kent, Gloucester, and Edmund. [Kent and Gloucester converse. Edmund stands back.]\n\n  Kent. I thought the King had more affected the Duke of Albany than Cornwall.\n  Glou. It did always seem so to us; but now, in the division of the ...<\/pre>\n\n\n\n
Looking at the beginning of this scene, in order to perform text analysis without considering who is talking (often abbreviated, which makes analysis even stranger), data cleaning will change the above text to as follows.<\/p>\n\n\n\n
ACT I. Scene I.\n[King Lear's Palace.]\n\nEnter Kent, Gloucester, and Edmund. [Kent and Gloucester converse. Edmund stands back.]\n\n  I thought the King had more affected the Duke of Albany than Cornwall.\n  It did always seem so to us; but now, in the division of the ...<\/pre>\n\n\n\n
Evidently, this makes reading or performing the play harder; however, it also ensures that the data will not be skewed by the structure of the play. Other name cleaning is sometimes necessary, but only the speaker name is removed (not, for instance, stage directions). Thus it is also possible to analyze character movements, but this can be easily separated out as connected to specific words, like “Enter” or “Exeunt.” (Name cleaning was done via OpenRefine, a free software for cleaning data).<\/p>\n\n\n\n
Several graphs are presented below — the first two use all of the data I was originally planning to use, just to get a grip on how that would look, but the last three have to do with examining trends within specific texts. I found that to be much more helpful than having them all lumped together, at least generally.<\/p>\n\n\n\n
Analysis of the text is provided through Voyant Tools<\/a> (linked).<\/p>\n\n\n\n
If you are searching for the header image, it is a stock image and can be found at this link<\/a>. I make no claims of ownership.<\/p>\n\n\n\n
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ANALYSES<\/strong><\/p>\n\n\n