classificator

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?

More here: https://en.wikipedia.org/wiki/Naive_Bayes_classifier

Installing

Recommended: Node v6.0.0 +

npm install --save classificator

Usage

const bayes = require('classificator')
const classifier = bayes()

Teach your classifier

classifier.learn('amazing, awesome movie! Had a good time', 'positive')
classifier.learn('Buy my free viagra pill and get rich!', 'spam')
classifier.learn('I really hate dust and annoying cats', 'negative')
classifier.learn('LOL this sucks so hard', 'troll')

Make your classifier unlearn

classifier.learn('i hate mornings', 'positive');
// uh oh, that was mistake. Time to unlearn
classifier.unlearn('i hate mornings', 'positive');

Remove a category

classifier.removeCategory('troll');

categorization

classifier.categorize("I've always hated Martians");
// => {
        likelihoods: [
          {
            category: 'negative',
            logLikelihood: -17.241944258040537,
            logProba: -0.6196197927020783,
            proba: 0.538149006882628
          }, {
            category: 'positive',
            logLikelihood: -17.93509143860048,
            logProba: -1.312766973262022,
            proba: 0.26907450344131445
          }, {
            category: 'spam',
            logLikelihood: -18.26854831109384,
            logProba: -1.646223845755383,
            proba: 0.19277648967605832 }
        ],
        predictedCategory: 'negative'
      }

serialize the classifier's state as a JSON string.

let stateJson = classifier.toJson()

load the classifier back from its JSON representation.

let revivedClassifier = bayes.fromJson(stateJson)

note: stateJson can either be a JSON string (obtained from classifier.toJson()), or an object

---

API

let 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.

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

You can specify the alpha parameter of the additive smoothing operation. This is an integer. The default value is 1

You can also specify the fitPrior parameter. Defines how the prior probablity is calculated. If set to false, the classifier will use an uniform prior rather than a learnt one. The default value is true.

classifier.learn(text, category)

Teach your classifier what category should be associated with an array text of words.

classifier.unlearn(text, category)

The classifier will unlearn the text that was associated with category.

classifier.removeCategory(category)

The category is removed and the classifier data are updated accordingly.

classifier.categorize(text)

Parameters

text {String}

Returns

{Object} An object with the predictedCategory and an array of the categories ordered by likelihood (most likely first).

{
    likelihoods : [
      ...
      {
        category: 'positive',
        logLikelihood: -17.93509143860048,
        logProba: -1.312766973262022,
        proba: 0.26907450344131445
      },
      ...
    ],
    predictedCategory : 'negative'  //--> the main category bayes thinks text
                                          belongs to. As a string
}

classifier.toJson()

Returns the JSON representation of a classifier.

let classifier = bayes.fromJson(jsonStr)

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