https://github.com/malavbhavsar/sentimentalizer

Sentiment analysis with Machine Learning
https://github.com/malavbhavsar/sentimentalizer

machine-learning naive-bayes rails ruby sentiment-analysis

Last synced: about 1 month ago
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Sentiment analysis with Machine Learning

• Host: GitHub
• URL: https://github.com/malavbhavsar/sentimentalizer
• Owner: malavbhavsar
• License: mit
• Archived: true
• Created: 2012-11-03T18:14:42.000Z (about 12 years ago)
• Default Branch: master
• Last Pushed: 2023-05-01T16:43:30.000Z (over 1 year ago)
• Last Synced: 2024-09-21T13:29:24.273Z (about 2 months ago)
• Topics: machine-learning, naive-bayes, rails, ruby, sentiment-analysis
• Language: Ruby
• Size: 2.85 MB
• Stars: 165
• Watchers: 8
• Forks: 38
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Archived!

It is 2023. You should be using something else for Sentiment Analysis. Maybe [this](https://huggingface.co/blog/sentiment-analysis-python) is something you can use.

# sentimentalizer

Inspired by ~~[Sentan](https://github.com/martinrue/Sentan)~~ [node-sentiment](https://github.com/martinrue/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"``` 

```ruby

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 

```ruby

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

```ruby

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 

```ruby

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.