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• Bring text to explore in Voyant. Input format Format can be plain text, a PDF (with OCR), a MS Word Document or a URL (for HTML analysis of web pages). Upload of any material will be subject to the Voyant privacy policy, so bring text you can safely share.
• Bring a laptop to support your explorations.
• Look through this lesson plan, develop your questions - and bring them to class.
• Optionally - the intrepid may want to obtain a login to the HTRC Portal, create a workset and run a few algorithms in advance of this lecture. Documentation for obtaining a sign-on and documentation for the portal is will be helpful.

Going down the rabbit hole: anatomy of a digital book

How is a digital book made?  How does the structure relate to its function? What opportunities does this afford us in terms of text analysis?

(1) Consider this book: Alice's Adventures in Wonderland.  Explore the controls on the right side of the page turner.  

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• There is a box labeled "search in this text".  What can you deduce about the book from given this functionality? 
• What do the other controls do?  Is there a way to summarize this class of controls?  What underlying logic might you predict that coordinates these functions? (Food for thought...download and display in a browser.)

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(4) Diving deeper into text.  Optical Character Recognition (OCR) processes are not perfect. Consider some areas of special challenge:

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Computational analysis of text

We count tokens - What is tokenization?  Why tokenize?  What are some strategies used to tokenize?

(1) Let's look again at the Arapaho gospel of St. Luke.  Switch to text view.

• Is this OCR accurate to the visually captured page? 
• What is a word? How would you define this for "word" to a computer?
• What isn't a word? How would you tell a computer to exclude these?
• Consider languages with which you are familiar.  Can you think of special cases where tokens might contain more than one word?
• What sets of rules would we need in order to tokenize effectively?  Would these be ordered in any specific way?
• Is there a "right way" to tokenize?

(2) Discussion: What are the opportunity points that the structure and arrangement of a book afford?

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• Sample texts, courtesy of Project Gutenberg. Use the plain text version.
  
  • Crane, Stephen, 1871-1900. The Red Badge of Courage: An Episode of the American Civil War.
  • Dickens, Charles, 1812-1870. A Tale of Two Cities.
  • Upham, Charles Wentworth, 1802-1875. Salem Witchcraft, Volumes I and II
• Sample URLS: copy and paste into the Voyant upload browser window to get started.Sample URLS: copy and paste into the Voyant upload browser window to get started.

  • Economics of Crisis - http://www.economicsofcrisis.com/indications.html 

  • Instructions to major John Sullivan. Washington, George, 1732-1799. The writings of George Washington from the original manuscript sources. Electronic Text Center, University of Virginia Library. Internet Archive.

  • Copyright Law of the United States of America and Related Laws Contained in Title 17 of the United States Code - http://www.copyright.gov/title17/92preface.html
• Sample visualization: Dr. Martin Luther King, Jr. I Have a Dream speech.

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• Explore visualizations in the "dashboard" that results from analysis of uploaded text.  Change Explore changing the options for visualizations. 
• Discuss the relative merits of the various visualizations. 

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• We calculate frequency
  

  • We can express our counts simply (as counts), or we can express them as frequencies.  Why calculate frequencies?
  • Is either representation misleading?  If so, in what ways?
• What does exerting control do to our results?  Does it change the validity of our assertions?
• How should method be explained when making assertions from results?
• Who determines what is "signal" and what is "noise"What is signal?  What is noise?

Moving from Microanalysis to Macroanalysis (Google nGrams and HTRC bookworm)

nGrams are words or phrases, tokenized and counted in a defined corpus and displayed as a graph showing how relative frequencies of those phrases have occurred as occurring over publication date. The two tools referenced below provide a basis for exploration of ngrams.  Each tool is bound to secondary data derived from analysis of a different corpus, so results of the same nGram will not necessarily align. 

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• How easily can these examples above be refined and improved?
• Compare the two interfaces, especially as to the affordances and the limits of each. 
• What additional elements of control would be useful that aren't available?
• When we see unexpected wave forms, what do we make of these? 
  
  • Do these constitute discoveries or represent errors?  How can we distinguish?
  • Would the flaws be due to the data, the metadata, the algorithms?

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• Compare one collection of books to a second collection, and report the differences in frequency of tokens - ShksprDunning
• Extract entities from a set of books, and list out referents of where they occur
  
  • Person, location - WSPlaysEntityExtract
  • Dates - WSComediesExtractDates (Note the wide variety of what is considered a "date")
  • Dates visualized over timeline - WSComediesDateExtractSimileRemix
• Model "topics", or clusters of tokens that are statistically more likely to be found together - WSComediesTopics; WSTragediesTopics

Discussion

• In general, do the results look valid? Do any of these algorithms yield results that might be considered confusing or less than perfect?

• Note that results can be downloaded.  What might be advantages of this portability?Note the portability of the results.  Can you think of advantages to this? 
• Are there things that the researcher would want or need to know about these algorithms when making claims about results?

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