YES! These are pretty much exactly the methods I used when I developed my project
http://prosecraft.ioYou can see the emotional story arc -- the shapes of the stories -- for more than 16,000 books.
I train a Word2Vec model on the vocabulary of all those books (almost 1.5 billion words) and then I use a clustering algorithm to score all those words on a sentiment scale of 1 to 10 (where 1 is the most negative and 10 is the most positive). Then I break the books into 50 equal-sized chunks and aggregate the positive and negative scores for each chunk.
You can click on any of the chart segments to see a word cloud of all the words that contributed to the positive and negative sentiment of that chunk. You can really see the ups and downs of the stories, as the protagonists struggle to overcome their obstacles, when you look at those charts!
Here are a few of my favorite example books to show people:
The Hobbit
http://prosecraft.io/library/j-r-r-tolkien/the-hobbit/
Harry Potter and the Deathly Hallows
http://prosecraft.io/library/j-k-rowling/harry-potter-and-th...
Animal Farm
http://prosecraft.io/library/george-orwell/animal-farm/
I first encountered this method not through Vonnegut but through the "Hedonometer" project, at the University of Vermont Computational Story Lab. They use this technique on the twitter firehose, to measure the overall emotional arc of the world, as expressed in social media.
https://hedonometer.org/timeseries/en_all/
There's an excellent episode of the podcast Lexicon Valley where they discuss the hedonometer project, with the researchers at UVM who developed it...
http://www.slate.com/articles/podcasts/lexicon_valley/2015/0...
You can see the emotional story arc -- the shapes of the stories -- for more than 16,000 books.
I train a Word2Vec model on the vocabulary of all those books (almost 1.5 billion words) and then I use a clustering algorithm to score all those words on a sentiment scale of 1 to 10 (where 1 is the most negative and 10 is the most positive). Then I break the books into 50 equal-sized chunks and aggregate the positive and negative scores for each chunk.
You can click on any of the chart segments to see a word cloud of all the words that contributed to the positive and negative sentiment of that chunk. You can really see the ups and downs of the stories, as the protagonists struggle to overcome their obstacles, when you look at those charts!
Here are a few of my favorite example books to show people:
The Hobbit
http://prosecraft.io/library/j-r-r-tolkien/the-hobbit/
Harry Potter and the Deathly Hallows
http://prosecraft.io/library/j-k-rowling/harry-potter-and-th...
Animal Farm
http://prosecraft.io/library/george-orwell/animal-farm/
I first encountered this method not through Vonnegut but through the "Hedonometer" project, at the University of Vermont Computational Story Lab. They use this technique on the twitter firehose, to measure the overall emotional arc of the world, as expressed in social media.
https://hedonometer.org/timeseries/en_all/
There's an excellent episode of the podcast Lexicon Valley where they discuss the hedonometer project, with the researchers at UVM who developed it...
http://www.slate.com/articles/podcasts/lexicon_valley/2015/0...