Naive Bayes Classifier for node.js
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?
npm install bayes
var bayes = require'bayes'var classifier = bayes// teach it positive phrasesclassifierlearn'amazing, awesome movie!! Yeah!! Oh boy.' 'positive'classifierlearn'Sweet, this is incredibly, amazing, perfect, great!!' 'positive'// teach it a negative phraseclassifierlearn'terrible, shitty thing. Damn. Sucks!!' 'negative'// now ask it to categorize a document it has never seen beforeclassifiercategorize'awesome, cool, amazing!! Yay.'// => 'positive'// serialize the classifier's state as a JSON string.var stateJson = classifiertoJson// load the classifier back from its JSON representation.var revivedClassifier = bayesfromJsonstateJson
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. The default tokenizer removes punctuation and splits on spaces.
var classifier = bayesreturn textsplit' '
Teach your classifier what
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.
category it thinks
text belongs to. Its judgement is based on what you have taught it with .learn().
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
(The MIT License)
Copyright (c) by Tolga Tezel email@example.com
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.
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