Hmm, nobody's mentioned Horse_eComics, the web comic that takes Horse_eBooks tweets and turns them into comics:
Happy third birthday, Horse_ebooks
21–24 of 24 posts
Re: Happy third birthday, Horse_ebooks
#22She uses the phrase "spam robot". Is that semantically equivalent to automated? If so, for the love of god, can someone tell me how I can build something that auto-generates beautiful existential poetry like that? (I am only half joking here)
Have you played around with markov chains? They're really easy to build and produce very entertaining results - a few years ago I tried feeding all of the SXSW Interactive session titles in to one and got results like "Participatory budgeting crowdsourcing for real time marketing growing a digital culture".
Can you suggest some good resources for newbies/laymen on the topic?
Edit - For anyone else who might be looking, this page gives a nice pythonic example:
http://agiliq.com/blog/2009/06/generating-pseudo-random-text...
Re: Happy third birthday, Horse_ebooks
#23She uses the phrase "spam robot". Is that semantically equivalent to automated? If so, for the love of god, can someone tell me how I can build something that auto-generates beautiful existential poetry like that? (I am only half joking here)
As the name vaguely alludes to, a lot of the text is extracted from ebooks, including public domain ones from Project Gutenberg. That said, I find it hard to believe that just picking random sentence fragments would give such a high signal-to-noise ratio, so there's probably something else going on. If I were the one pulling the strings of Horse_ebooks, I would probably focus on building a statistical model that uses…
> If I were the one pulling the strings of Horse_ebooks, I would probably focus on building a statistical model that uses the number of favorites per tweet as its training data, and looks for linguistic features that are highly correlated with getting lots of favorites.
Yeah, but the problem in that approach is that you will always hit the cold start problem. What happens at the beginning when there is nothing? And no one likes you? If this was a robot, I can't imagine significant time was spent initially on custom designing these tweets.
Re: Happy third birthday, Horse_ebooks
#24Earlier quoted context omitted.
Have you played around with markov chains? They're really easy to build and produce very entertaining results - a few years ago I tried feeding all of the SXSW Interactive session titles in to one and got results like "Participatory budgeting crowdsourcing for real time marketing growing a digital culture".
Can you suggest some good resources for newbies/laymen on the topic? Edit - For anyone else who might be looking, this page gives a nice pythonic example: http://agiliq.com/blog/2009/06/generating-pseudo-random-text...
Well, as a very rough overview, all they are is:
* read in words
* keep track of the frequency of "wordA wordB", "wordB wordC", etc.
* use that frequency, e.g.:
{ "wordA" : {"wordB" : 0.5,
"wordC" : 0.25, ...},
"wordB" : {"wordC" : 0.75, ...} }
combined with a word, say "wordA", and pick a
random word from the frequencies, weighted by frequency.
* your word is now the word you just picked. repeat that
last step until you get bored.
The more you collect, the more 'real' your generation will tend to be, but it takes more data to train it effectively since you want lots of possibilities for each 'key' so it doesn't repeat itself. Otherwise it might think that the only thing that comes after 'key' so it doesn't repeat itself. Otherwise it might think that the only thing that comes after 'key' so it doesn't repeat itself. Otherwise it might think that the only thing that comes after 'key' is a single phrase, so it keeps selecting it and it doesn't sound like something anyone would actually say.