NaiveBayesClassifier is an implementation of a Multinomial Naive-Bayes Classifier that uses Laplace Smoothing. It can take in a piece of text and tell you which category it most likely belongs to.

I haven't really touched this in a few years... maybe I'll come back to it one day.

v0.3.0 IS COMING SOON

I'm working on the next version of NaiveBayesClassifer. Here are some of the new features that you can expect:

• ✓ Reduced memory (heap) footprint by using Streams.
• ✓ Refactor to ES2015 and use "more appropriate" data-structures.
• Optional persistence, probably using REDIS.
• Asynchronous learning and classifying, using Promises.
• Categorisation speed improvements, using parallelisation via WebWorkers.
• Official node/iojs, Chrome OS and Firefox OS support. Other browsers or short-lived apps should use a web-service.

What is this good for?

"In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features." - Wikipedia: Naive Bayes classifier.

You can use this implementation for categorizing any text content into any arbitrary set of categories. For example:

• is an email spam, or not spam?
• is a news article about technology, politics, or sports?
• is some piece of text javascript or objective-c?
• is a tweet expressing positive sentiment or negative sentiment?

Depending on your specific attributes and sample size, there may be other algorithms that are better suited: Comparison of Classification Methods Based on the Type of Attributes and Sample Size.

Try it now

You can experiment, test and play with NaiveBayesClassifier in your browser at http://jsbin.com/xixuga/1/edit?html,js,console

If you would like to try NaiveBayesClassifier as a web-service, you can use: http://nbcaas.herokuapp.com/

Installing NaiveBayesClassifier

NaiveBayesClassifier is shipped in UMD format, meaning that it is available as a CommonJS/AMD module or browser global. You can install it using npm:

$ npm install naivebayesclassifier

OR using bower:

$ bower install naivebayesclassifier

Basic Usage

new NaiveBayesClassifier([options])

Using the default tokenization function, which splits on spaces:

var NaiveBayesClassifier = require('NaiveBayesClassifier'),
	classifier = new NaiveBayesClassifier();

Or with an optional custom tokenization function that you specify:

var NaiveBayesClassifier = require('NaiveBayesClassifier');
var splitOnChar = function(text) { 
	return text.split('');
};
var classifier = new NaiveBayesClassifier({ tokenizer: splitOnChar });

.withClassifier(classifier)

Recover an existing classifier, which you may have retrieved from a database or localstorage:

var NaiveBayesClassifier = require('NaiveBayesClassifier'),
	classifier = NaiveBayesClassifier.withClassifier(existingClassifier);

.learn(text, category)

Teach your classifier what category the text belongs to. The more you teach your classifier, the more reliable it becomes. It will use what it has learned to identify new documents that it hasn't seen before.

classifier.learn('amazing, awesome movie!! Yeah!!', 'positive');
classifier.learn('terrible, shitty thing. Damn. Sucks!!', 'negative');
classifier.learn('I dont really know what to make of this.', 'neutral');

.categorize(text)

classifier.categorize('awesome, cool, amazing!! Yay.');

This will return the most likely category it thinks text belongs to and its probability. Its judgement is based on what you have taught it with .learn(text, category).

{ 
  "category": "positive",
  "probability": 0.7687012152002337,
  "categories":
   { 
     "positive": 0.7687012152002337,
     "negative": 0.15669449587155299,
     "neutral": 0.07460428892821332
   } 
}

Complete API Documentation

If you would like to explore the full API, you can find auto-generated documentation at: https://hadi.io/NaiveBayesClassifier.

Acknowledgements

The theory and implementation used in this library is based on the Stanford Natural Language Processing Coursera class, by Professor Dan Jurafsky & Chris Manning.

License

Copyright (C) 2015, Hadi Michael. All rights reserved.

Licensed under BSD-3-Clause