Bayesian Classification on Rails

26 Jan 2010

A project I’ve been working on watches Twitter for some search keywords, with the goal of finding new customers, jobs, items for sale, etc. For example, a computer repair shop might want to watch for the keywords “laptop” and “broken”, and then reply to tweets where they think they can help.

But as anyone who uses Twitter can attest, even with some very specific search terms, language filtering and geocoding, there is going to be a lot of white noise. I decided to take this one step further.

Bayesian classification (your garden-variety spam filter) in ruby is quite easy, thanks to ruby-stemmer and the excellent classifier gem. The canonical example:

require 'classifier'
b = Classifier::Bayes.new :categories => ['Interesting', 'Uninteresting']
b.train_interesting "here are some good words. I hope you love them"
b.train_uninteresting "here are some bad words, I hate you"
b.classify "I hate bad words and you" # returns 'Uninteresting'

Of course, if you’re implementing this in a Rails application, chances are you want the classifier to learn from real data over time. In my case, I want it to learn that a tweet is uninteresting when I delete it, and I want it to learn that a tweet is interesting when I visit the Tweet#show action.

It seems the usual method is to marshal the classifier object with madeleine, which creates a new snapshot file each time you train it. This is both easy and fast, but we’re going to end up with thousands or millions of snapshot files in no time flat. Additionally, all bets are off if we have a few users who are really into cheap viagra. We need to give each User his own classifier and let him train it over time.

First, let’s set up our environment. Grab the latest ruby-stemmer and classifier gems from Github, and build them from source. I recommend this because the gem versions I got on my first try were way out of date and quite broken, and because you’ll need a classifier fork with my remove_stemmer method to marshal your classifiers using ActiveRecord.

$ git clone git://github.com/aurelian/ruby-stemmer.git
$ cd ruby-stemmer
$ rake compile
$ sudo rake install
$ cd ..

$ git clone git://github.com/logankoester/classifier.git
$ cd classifier
$ sudo rake install

$ sudo gem install twitter

Generate a fresh rails app if you want to follow along.

$ rails classifier_rails_example
$ script/generate resource user id:integer classifier:text
$ script/generate resource keyword id:integer user_id:integer text:string
$ script/generate resource tweet id:integer keyword_id:integer user_id:integer \
  text:string read:boolean interesting:boolean
$ script/generate migration ChangeClassifierDefaults

Alternatively, you can clone the code from this tutorial with:

$ git clone git@github.com:logankoester/classifier_rails_example.git

Now edit the migration you just created to look like this:

class ChangeClassifierDefaults < ActiveRecord::Migration
  def self.up
    change_column :tweets, :interesting, :boolean, :default => false
    change_column :tweets, :read, :boolean, :default => false
    change_column :keywords, :text, :string, :default => ""
  end

def self.down
change_column :keywords, :text
change_column :tweets, :read
change_column :tweets, :interesting
end

end

…and run it

$ rake db:migrate

Open your config/environment.rb file, and add the following gems to the Initializer block.

config.gem 'ruby-stemmer', :lib => 'lingua/stemmer'
config.gem 'luisparravicini-classifier', :lib => 'classifier'
config.gem 'twitter'

Now we can use ActiveRecord’s built-in YAML serialization to store the classifier.

class User < ActiveRecord::Base
  has_many :tweets
  has_many :keywords

  serialize     :classifier, Classifier::Bayes
  before_create :initialize_classifier
  before_update :remove_stemmer

private

  def initialize_classifier
    self.classifier = Classifier::Bayes.new(
      :categories => ['Interesting', 'Uninteresting']
    )
    remove_stemmer
  end

  def remove_stemmer
    self.classifier.remove_stemmer
  end
end

The remove_stemmer method requires a little explanation. When a Classifier is initialized, it also creates a Stemmer object to use, which ordinarily gets marshalled along with its Classifier. But when demarshalled later, the Stemmer object (which is really just a C extension) will get caught with its shorts down, and either throw an error like “Stemmer is not initialized”, or in older versions, simply segfault your rails environment!

The solution is simple; my fork implements a remove_stemmer method on Classifier::Base, which will force the stemmer to be reinitialized the next time it is needed. Call this method before you marshal your classifier, and your troubles will melt away.

Moving on to the Tweet model, we want to classify each tweet when it is created.

class Tweet < ActiveRecord::Base
  belongs_to :user
  belongs_to :keyword

  before_save :classify
  
  def classify
    text = self.text.gsub /#{self.keyword.text}/, ''
    if self.user.classifier.classify(text) == 'Interesting'
      self.interesting = true
    end
  end
end

Of course, we don’t want to throw off the results by including a word which is going to occur in every tweet, so we remove the search term from the text prior to classification.

Add a little method to your Keyword model to grab new tweets from the Twitter Search API

class Keyword < ActiveRecord::Base
  belongs_to :user
  has_many   :tweets, :dependent => :destroy

  after_save :search

  def search
    search = Twitter::Search.new(self.text).fetch
    search.results.each do |r|
      t = Tweet.create(
        :keyword => self,
        :user_id => self.user,
        :text => r.text
      )
      t.save
    end
  end
end

Almost done! Now we need to train our sweet new classifier. I’ve opted to do this entirely from the controller, so that messing around in the console won’t inadvertently have an impact on the machine’s learning. We also want to mark the tweet in question as already read, so that the lesson is only learned once.

class TweetsController < ApplicationController

  def show
    @tweet = Tweet.find(params[:id])
    unless @tweet.read?
      current_user.classifier.train_interesting(
        @tweet.text.gsub(/#{@tweet.keyword.text}/, '')
      )
      current_user.save
      @tweet.read = true
      @tweet.save
    end
  end


  def destroy
    if @tweet = Tweet.find(params[:id])
      if @tweet.destroy
        current_user.classifier.train_uninteresting(
          @tweet.text.gsub(/#{@tweet.keyword.text}/, '')
        )
        current_user.save
      end
    end
  end

end

And there you have it… a simple machine learning solution for extracting awesome tweets. Let’s try it out!

Fire up a script/console session.

Loading development environment (Rails 2.3.5)
>> u = User.create
=> #<User id: 1, classifier: #<Classifier::Bayes:0xb64f2354 
  @categories={:Uninteresting=>{}, :Interesting=>{}}, total_words0, 
  stemmernil, options{:encoding=>"UTF_8", 
  :categories=>["Interesting", "Uninteresting"], 
  :language=>"en"}, created_at: "2010-01-26 22:17:19", 
  updated_at: "2010-01-26 22:17:19"

As you can see, our new user has a Bayesian Classifier waiting around to learn what kind of tweets he likes.


>> u.keywords.create(:text => "robots")
=> #<Keyword id: 1, user_id: 1, text: "robots", created_at: "2010-01-26 22:20:55", 
  updated_at: "2010-01-26 22:20:55">
>> Tweet.all.size
=> 15

You can use the following oneliners from script/console to play around with the training:

Keyword.all.each {|k| k.search} # Rerun all searches to grab and classify more results

# Any tweet with the word "cyborgs" is interesting
Tweet.all.each { |t| 
  u.classifier.train_interesting(t.text) if t.text.downcase.include? "cyborgs"
}

# Any tweet with the word "discount" is uninteresting
Tweet.all.each { |t| 
  u.classifier.train_uninteresting(t.text) if t.text.downcase.include? "discount"
} 

# Print the interesting tweets and count them
Tweet.find_all_by_interesting(true).each do |t| pp t.text }.size 

# Rerun the classification on every tweet
Tweet.all.each {|t| t.classify }

Of course, this technique can be applied to sorting pretty much any kind of text. Interesting/uninteresting tweets are just one example from my life. Start hacking!

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