Naive Bayes Text Classifier

Build Status Coverage Status Gitter

Classify text, analyse sentiments, recognize user intents for chatbot using wink-naive-bayes-text-classifier. Its API offers a rich set of features including cross validation to compute confusion matrix, precision, and recall. It delivers impressive accuracy levels with right text pre-processing using wink-nlp:

Dataset Accuracy
Sentiment Analysis
Amazon Product Review Sentiment Labelled Sentences Data Set at UCI Machine Learning Repository
800 training examples & 200 validation reviews

Refer to sentiment-analysis-example directory for the reference code
Intent Classification
Chatbot corpus from NLU Evaluation Corpora as mentioned in paper titled Evaluating Natural Language Understanding Services for Conversational Question Answering Systems
100 training examples & 106 validation

Refer to chatbot-example directory for the reference code

Text Pre-processing

A winkNLP based helper function for general purpose text pre-processing is available that (a) tokenizes, (b) removes punctuations, symbols, numerals, URLs, stop words, (c) stems each token and (d) handles negations. It can be required from wink-naive-bayes-text-classifier/src/prep-text.js. WinkNLP's Named Entity Recognition may be used to further enhance the pre-processing.


These include smoothing factor to control additive smoothing and a consider presence only flag to choose from Multinomial/Binarized naive bayes.

The trained model can be exported as JSON and can be reloaded later for predictions.


Use npm to install:

npm install wink-naive-bayes-text-classifier --save

It requires Node.js version 16.x or 18.x.


// Load Naive Bayes Text Classifier
var Classifier = require( 'wink-naive-bayes-text-classifier' );
// Instantiate
var nbc = Classifier();
// Load wink nlp and its model
const winkNLP = require( 'wink-nlp' );
// Load language model
const model = require( 'wink-eng-lite-web-model' );
const nlp = winkNLP( model );
const its = nlp.its;

const prepTask = function ( text ) {
  const tokens = [];
      // Use only words ignoring punctuations etc and from them remove stop words
      .filter( (t) => ( t.out(its.type) === 'word' && !t.out(its.stopWordFlag) ) )
      // Handle negation and extract stem of the word
      .each( (t) => tokens.push( (t.out(its.negationFlag)) ? '!' + t.out(its.stem) : t.out(its.stem) ) );

  return tokens;
nbc.definePrepTasks( [ prepTask ] );
// Configure behavior
nbc.defineConfig( { considerOnlyPresence: true, smoothingFactor: 0.5 } );
// Train!
nbc.learn( 'I want to prepay my loan', 'prepay' );
nbc.learn( 'I want to close my loan', 'prepay' );
nbc.learn( 'I want to foreclose my loan', 'prepay' );
nbc.learn( 'I would like to pay the loan balance', 'prepay' );

nbc.learn( 'I would like to borrow money to buy a vehicle', 'autoloan' );
nbc.learn( 'I need loan for car', 'autoloan' );
nbc.learn( 'I need loan for a new vehicle', 'autoloan' );
nbc.learn( 'I need loan for a new mobike', 'autoloan' );
nbc.learn( 'I need money for a new car', 'autoloan' );
// Consolidate all the training!!
// Start predicting...
console.log( nbc.predict( 'I would like to borrow 50000 to buy a new Audi R8 in New York' ) );
// -> autoloan
console.log( nbc.predict( 'I want to pay my car loan early' ) );
// -> prepay

Try experimenting with this example on Runkit in the browser.


Check out the Naive Bayes Text Classifier API documentation to learn more.

Need Help?

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

About wink

Wink is a family of open source packages for Natural Language Processing, Statistical Analysis and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.

Copyright & License

wink-naive-bayes-text-classifier is copyright 2017-22 GRAYPE Systems Private Limited.

It is licensed under the terms of the MIT License.