Sentiment analysis with Machine Learning
It is 2023. You should be using something else for Sentiment Analysis. Maybe this is something you can use.
Inspired by Sentan node-sentiment.
This gem can be used separately or integrated with rails app.
Install gem using bundler gem "sentimentalizer"
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.
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\":\":)\"}"
Install gem using bundler gem "sentimentalizer"
Either fire up irb
, or require it in your project with require 'sentimentalizer'
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\":\":)\"}"
Copyright © 2018 malavbhavsar. See LICENSE.txt for
further details.