• This repository has been archived on 01/May/2023
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  • Rank 228,906 (Top 5 %)
  • Language
    Ruby
  • License
    MIT License
  • Created about 12 years ago
  • Updated over 1 year ago

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Repository Details

Sentiment analysis with Machine Learning

Archived!

It is 2023. You should be using something else for Sentiment Analysis. Maybe this is something you can use.

sentimentalizer

Inspired by Sentan node-sentiment.

This gem can be used separately or integrated with rails app.

Instructions for Rails use

  1. Install gem using bundler gem "sentimentalizer"

  2. Run rails g sentimentalizer. This will generate an initializer file with after_initialize hook for rails. It's basically training a model to use in the application. It will run everytime you start server or run any rake commands, would love some input on this.

  3. Now, you can run following after require "sentimentalizer"

Sentimentalizer.analyze('message or tweet or status')
# or for json output
Sentimentalizer.analyze('message or tweet or status', true)

You will get output like this

Sentimentalizer.analyze('i am so happy')
=> {'text' => 'i am so happy', 'probability' => '0.937', 'sentiment' => ':)' }
Sentimentalizer.analyze('i am so happy', true)
=> "{\"text\":\"i am so happy\",\"probability\":\"0.937\",\"sentiment\":\":)\"}"

Instructions for Vanilla Ruby use

  1. Install gem using bundler gem "sentimentalizer"

  2. Either fire up irb, or require it in your project with require 'sentimentalizer'

  3. Now, you need to train the engine in order to use it

require "sentimentalizer"

Sentimentalizer.setup

# or, wrap it in a class so setup can be automatic
class Analyzer
  def initialize
    Sentimentalizer.setup
  end
  
  def process(phrase)
    Sentimentalizer.analyze phrase
  end
end

# or for json output
Sentimentalizer.analyze('message or tweet or status', true)

And now you will get output like this

analyzer = Analyzer.new
analyzer.process('i am so happy')
=> {'text' => 'i am so happy', 'probability' => '0.937', 'sentiment' => ':)' }
analyzer.process('i am so happy', true)
=> "{\"text\":\"i am so happy\",\"probability\":\"0.937\",\"sentiment\":\":)\"}"

Contributing to sentimentalizer

  • Check out the latest master to make sure the feature hasn't been implemented or the bug hasn't been fixed yet.
  • Check out the issue tracker to make sure someone already hasn't requested it and/or contributed it.
  • Fork the project.
  • Start a feature/bugfix branch.
  • Commit and push until you are happy with your contribution.
  • Make sure to add tests for it. This is important so I don't break it in a future version unintentionally.
  • Please try not to mess with the Rakefile, version, or history. If you want to have your own version, or is otherwise necessary, that is fine, but please isolate to its own commit so I can cherry-pick around it.

Copyright

Copyright (c) 2018 malavbhavsar. See LICENSE.txt for further details.