scrape_r_rational/data/cache/2qgdvs.md

[EDU] IBM Watson's User Modeling tech models characters in The Lord Of The Rings

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• u/alexanderwales:

  [Visualization from my last 474 reddit comments.](http://i.imgur.com/SgSvKVZ.png) I am quite curious how these numbers were arrived at, since some of it seems off to me - I don't trust a 99% in anything.
  
  **General instructions for doing this yourself**
  
  1. Go to http://pastebin.com/j1QxzKiR and copy-paste that script into notepad++ then save it as "whatever.py".
  
  2. Download Python 2.x from someplace like http://www.python.org/getit/releases/2.7/ and then install it.
  
  3. Run the python file from the command line (see here: http://mail.python.org/pipermail/tutor/2004-July/030634.html) and input your username.
  
  4. This will create a file called "username".txt (for example, keltranis.txt or I_RAPE_CATS.txt), but might take some time - just let it run until it's done.
  
  5. Now that your comment history is saved as a .txt file, go into Notepad++ and strip out all of the stuff that is not part of your actual commenting. This is the hard part, because you have to learn a bit of the context stuff. This was of great help: http://markantoniou.blogspot.com/2008/06/notepad-how-to-use-regular-expressions.html
  
  (I use a somewhat different setup now, but if you have some programming knowledge that should be enough to set you on the right path.)
  

• u/PixelDust73:

  There is no way Bilbo is more neurotic than Gollem!
  

• u/DataPacRat:

  Is there any actual use that these numbers can be put to?
  
  For example, say I put the first three books of SI into the machine, and sorted the various categories out by how extreme they were (eg, both 99% and 1% at the top)... what would I actually learn from the results, or be able to do that I couldn't have done before?
  

  • u/alexanderwales:

    I believe that an intended use-case is something like this:
    
    * Gather up a thousand different user profiles.
    * Run them through this program.
    * Find correlations between these models and another data set (say, those users favorite movies - you should have both if you scraped from Facebook).
    * Use those correlations to build a recommendation engine.
    
    For a single user (or a single book), I doubt it's of much use, but assuming that its results have any validity, it would be useful for grouping similar people.
    

  • u/xamueljones:

    It doesn't seem very useful for you unless you understand very clearly (on the level of a trained psychologist) the meaning of each personality trait and how they interact. Knowing how your character is being portrayed is a useful thing to know, but how can you actually take advantage of this? The only thing I can think of is to see whose personality is changing in ways you aren't intending, but you seem to be a good enough writer to understand that it's more fun to let your characters do what they want instead of shoehorning them into contrived situations.
    

• u/E-o_o-3:

  I put in two blocks of text I've written,and there wasn't a high level of agreement, but maybe they just weren't large enough.
  
  How did they determine which words were "neurotic" and so on?
  

• u/None:

  fascinating, thanks. crossposted over at r/tolkienfans
  

• u/Gurkenglas:

  Running this on yourself and checking whether the results apply to you seems like listening to cold reading.
  

  • u/AmeteurOpinions:

    Running it on just yourself is mostly useless. You want to run it on all of Reddit and *then* start comparing numbers.
    

• u/traverseda:

  I'm really not clear on what "self-transcendence" means.
  

• u/None:

  [deleted]
  

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