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W I T H A L I T T L E B I T O F H E L P F R O M M A R Y
S H E L L E Y ’ S “ F R A N K E N S T E I N ”
A Dissection of Voyant Tools
N O T F R A N K E N S T E I N - > < - F R A N K E N S T E I N
By Kelsey Patterson1
But What’s “Frankenstein”?
1 2
But What’s “Voyeur”?
 Accommodating
 Easy to use
 Analyze texts
 Beta Stage
Beta?
1 2 3 * I understand the pronunciation of betta fish
LET’S GET STARTED
STOP LIST
1
STOP LIST
1
1 2 3 4 5
Time
Little Bit of Extra Context
Monster vs. Creature
Monster vs. Creature
Relative Frequencies
1. Select like words 2. Graph!
But What About Lemmas?
Where’s Clerval?
Introduction to Henry
Clerval and his
background relationship
w/ Victor
Clerval’s nurses an ill
Frankenstein and joins
him on a journey
Clerval’s untimely end
Where’s Clerval?
Introduction to Henry
Clerval and his
background relationship
w/ Victor
Clerval’s nurses an ill
Frankenstein and joins
him on a journey
Clerval’s untimely end
Playing Favorites
Have something you want
to remember? Just click on
that heart with plus!
The heart to the left will
take you back to your list of
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Pecha Kucha - Kelsey Patterson

Hinweis der Redaktion

  1. Hi, my name’s Kelsey and I’m here to take you on a tour of the text analysis website “Voyant Tools”. You’ll be able to see how this resource can be used to discover symbols, plot points, and more all in one place. Along the way, we’ll get a little help from Mary Shelley’s “Frankenstein”.
  2. Born in 1797. Hung in the literary circle of Romantic greats such as Lord Byron and her husband Percy Bysshe Shelley. Tells the story of Victor Frankenstein’s horrific experiment of building a monster from cadaver parts. The plot follows the path of the creature’s destruction as well as Victor’s inability to own up to his mistake and take responsibility.
  3. Accomodating:use texts in a variety of formats including plain text, HTML, XML, PDF, RTF and MS Worduse texts from different locations, including URLs and uploaded files
  4. lack of more advanced linguistic processing (lemmatization, parts of speech, semantic awareness)lack of XML-aware analytic features (though XML is a valid input format)the current default skin (configuration of tools) is not well-suited to reading textssome of the user documentation is a bit bareother funcitonality:proximity searching of termsmulti-word (n-gram) views (though you can search for specific phrases)
  5. The start screen. When all components are open, the screen consists of three columns with the text set in the middle. Both side columns have three windows which all utilize different tools to analyze the one text. It’s a lot to handle at once, so why don’t we magnify just one section.
  6. Got rid of common english words as well as said and shall
  7. Got rid of common english words as well as said and shall
  8. Key word in context
  9. Raw frequency
  10. Raw frequency
  11. Relative frequencies = how often the word occurs in proportion to the rest of the words in that section