twitter_ebooks 2.1.5
Rewrite of my twitter_ebooks code. While the original was solely a tweeting Markov generator, this framework helps you build any kind of interactive twitterbot which responds to mentions/DMs.
Installation
gem install twitter_ebooks
Setting up a bot
Run ebooks new <reponame>
to generate a new repository containing a sample bots.rb file, which looks like this:
# This is an example bot definition with event handlers commented out
# You can define as many of these as you like; they will run simultaneously
Ebooks::Bot.new("abby_ebooks") do |bot|
# Consumer details come from registering an app at https://dev.twitter.com/
# OAuth details can be fetched with https://github.com/marcel/twurl
bot.consumer_key = "" # Your app consumer key
bot.consumer_secret = "" # Your app consumer secret
bot.oauth_token = "" # Token connecting the app to this account
bot.oauth_token_secret = "" # Secret connecting the app to this account
bot. do |dm|
# Reply to a DM
# bot.reply(dm, "secret secrets")
end
bot.on_follow do |user|
# Follow a user back
# bot.follow(user[:screen_name])
end
bot.on_mention do |tweet, |
# Reply to a mention
# bot.reply(tweet, meta[:reply_prefix] + "oh hullo")
end
bot.on_timeline do |tweet, |
# Reply to a tweet in the bot's timeline
# bot.reply(tweet, meta[:reply_prefix] + "nice tweet")
end
bot.scheduler.every '24h' do
# Tweet something every 24 hours
# See https://github.com/jmettraux/rufus-scheduler
# bot.tweet("hi")
end
end
Bots defined like this can be spawned by executing run.rb
in the same directory, and will operate together in a single eventmachine loop. The easiest way to run bots in a semi-permanent fashion is with Heroku; just make an app, push the bot repository to it, enable a worker process in the web interface and it ought to chug along merrily forever.
Archiving accounts
twitter_ebooks comes with a syncing tool to download and then incrementally update a local json archive of a user's tweets.
➜ ebooks-ebooks git:(master) ebooks archive 0xabad1dea corpus/0xabad1dea.json
Currently 20209 tweets for 0xabad1dea
Received 67 new tweets
The first time you'll run this, it'll ask for auth details to connect with. Due to API limitations, for users with high numbers of tweets it may not be possible to get their entire history in the initial download. However, so long as you run it frequently enough you can maintain a perfect copy indefinitely into the future.
Text models
In order to use the included text modeling, you'll first need to preprocess your archive into a more efficient form:
➜ ebooks-ebooks git:(master) ebooks consume corpus/0xabad1dea.json
Reading json corpus from corpus/0xabad1dea.json
Removing commented lines and sorting mentions
Segmenting text into sentences
Tokenizing 7075 statements and 17947 mentions
Ranking keywords
Corpus consumed to model/0xabad1dea.model
Notably, this works with both json tweet archives and plaintext files (based on file extension), so you can make a model out of any kind of text.
Once you have a model, the primary use is to produce statements and related responses to input, using a pseudo-Markov generator:
> model = Ebooks::Model.load("model/0xabad1dea.model")
> model.make_statement(140)
=> "My Terrible Netbook may be the kind of person who buys Starbucks, but this Rackspace vuln is pretty straight up a backdoor"
> model.make_response("The NSA is coming!", 130)
=> "Hey - someone who claims to be an NSA conspiracy"
The secondary function is the "interesting keywords" list. For example, I use this to determine whether a bot wants to fav/retweet/reply to something in its timeline:
top100 = model.keywords.top(100)
tokens = Ebooks::NLP.tokenize(tweet[:text])
if tokens.find { |t| top100.include?(t) }
bot.twitter.favorite(tweet[:id])
end
Other notes
If you're using Heroku, which has no persistent filesystem, automating the process of archiving, consuming and updating can be tricky. My current solution is just a daily cron job which commits and pushes for me, which is pretty hacky.