Ruby gem that calculates TF-IDF out of a text to find most relevant words in each document of the corpus
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ruby-tf-idf.gemspec initial commit Jan 3, 2013


This gem calculates TF-IDF to find the most relevant words of each document in corpus

TF-IDF is for Term Frequency - Inverse Document Frequency


Add this line to your application's Gemfile:

gem 'ruby-tf-idf'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install ruby-tf-idf


require 'rubygems'
require 'ruby-tf-idf'

corpus = 
'A big enough hammer can usually fix anything',
'A bird in the hand is a big mistake .',
'A bird in the hand is better than one overhead!',
'A career is a job that takes about 20 more hours a week.',
'A clean desk is a sign of a cluttered desk drawer.',
'A cynic smells flowers and looks for the casket.'

limit = 3 #restrict to the top 3 relevant words per document
exclude_stop_words = false

@t =,limit,exclude_stop_words)
output =  @t.tf_idf

output = [ {"anything"=>0.7781512503836436, "fix"=>0.7781512503836436, "enough"=>0.7781512503836436}, {"mistake"=>0.7781512503836436, "bird"=>0.47712125471966244, "in"=>0.47712125471966244}, {"overhead!"=>0.7781512503836436, "better"=>0.7781512503836436, "one"=>0.7781512503836436}, {"week"=>0.7781512503836436, "career"=>0.7781512503836436, "hours"=>0.7781512503836436}, {"desk"=>1.5563025007672873, "drawer"=>0.7781512503836436, "clean"=>0.7781512503836436}, {"casket"=>0.7781512503836436, "cynic"=>0.7781512503836436, "smells"=>0.7781512503836436}, ]


  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request