bayes: A Naive-Bayes classifier for node.js

bayes takes a document (piece of text), and tells you what category that document belongs to.

What can I use this for?

You can use this 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 a piece of text expressing positive emotions, or negative emotions?

Installing

npm install bayes

Usage

var bayes = require('bayes')

var classifier = bayes()

// teach it positive phrases

classifier.learn('amazing, awesome movie!! Yeah!! Oh boy.', 'positive')
classifier.learn('Sweet, this is incredibly, amazing, perfect, great!!', 'positive')

// teach it a negative phrase

classifier.learn('terrible, shitty thing. Damn. Sucks!!', 'negative')

// now ask it to categorize a document it has never seen before

classifier.categorize('awesome, cool, amazing!! Yay.')
// => 'positive'

// serialize the classifier's state as a JSON string.
var stateJson = classifier.toJson()

// load the classifier back from its JSON representation.
var revivedClassifier = bayes.fromJson(stateJson)

API

var classifier = bayes([options])

Returns an instance of a Naive-Bayes Classifier.

Pass in an optional options object to configure the instance. If you specify a tokenizer function in options, it will be used as the instance's tokenizer. It receives a (string) text argument - this is the string value that is passed in by you when you call .learn() or .categorize(). It must return an array of tokens. The default tokenizer removes punctuation and splits on spaces.

Eg.

var classifier = bayes({
    tokenizer: function (text) { return text.split(' ') }
})

classifier.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.categorize(text)

Returns the category it thinks text belongs to. Its judgement is based on what you have taught it with .learn().

###classifier.probabilities(text)

Returns an array of { value, category } objects with probability calculated for each category. Its judgement is based on what you have taught it with .learn().

###classifier.toJson()

Returns the JSON representation of a classifier.

var classifier = bayes.fromJson(jsonStr)

Returns a classifier instance from the JSON representation. Use this with the JSON representation obtained from classifier.toJson()

License

(The MIT License)

Copyright (c) by Tolga Tezel [email protected]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.