text

Text Classification

import "github.com/Fabse333/goml/text"

This package implements text classification algorithms. For algorithms that could be used numberically (most/all of them,) this package makes working with text documents easier than hand-rolling a bag-of-words model and integrating it with other models

implemented models

• multiclass naive bayes
• term frequency - inverse document frequency
  • this model lets you easily calculate keywords from documents, as well as general importance scores for any word (with it's document) that you can throw at it!
  • because this is so similar to Bayes under the hood, you train TFIDF by casting a trained Bayes model to it such as tfidf := TFIDF(*myNaiveBayesModel)

example online naive bayes sentiment analysis

This is the general text classification example from the GoDoc package comment. Look there and at the tests for more detailed and varied examples of usage:

// create the channel of data and errors
stream := make(chan base.TextDatapoint, 100)
errors := make(chan error)

// make a new NaiveBayes model with
// 2 classes expected (classes in
// datapoints will now expect {0,1}.
// in general, given n as the classes
// variable, the model will expect
// datapoint classes in {0,...,n-1})
//
// Note that the model is filtering
// the text to omit anything except
// words and numbers (and spaces
// obviously)
model := NewNaiveBayes(stream, 2, base.OnlyWordsAndNumbers)

go model.OnlineLearn(errors)

stream <- base.TextDatapoint{
	X: "I love the city",
	Y: 1,
}

stream <- base.TextDatapoint{
	X: "I hate Los Angeles",
	Y: 0,
}

stream <- base.TextDatapoint{
	X: "My mother is not a nice lady",
	Y: 0,
}

close(stream)

for {
	err, more := <- errors
	if err != nil {
		fmt.Printf("Error passed: %v", err)
	} else {
		// training is done!
		break
	}
}

// now you can predict like normal
class := model.Predict("My mother is in Los Angeles") // 0