autoTagger.js

Simple JavaScript text auto tagger

Usage

Include the javascript in the browser:

``

html





or use it in Node.js:

js

$> npm install auto-tagger

js

var autoTagger = require('auto-tagger');





You can use the folowing methods to extract relevant tags from documents:

js

var testText = "This text is from a Wikipedia entry about Bayes' Theorem. Bayesian inference has applications in artificial intelligence and expert systems. Bayesian inference techniques have been a fundamental part of computerized pattern recognition techniques since the late 1950s. There is also an ever growing connection between Bayesian methods and simulation-based Monte Carlo techniques since complex models cannot be processed in closed form by a Bayesian analysis, while the graphical model structure inherent to statistical models, may allow for efficient simulation algorithms like the Gibbs sampling and other Metropolis-Hastings algorithm schemes. Recently Bayesian inference has gained popularity amongst the phylogenetics community for these reasons; applications such as BEAST, MrBayes and P4 allow many demographic and evolutionary parameters to be estimated simultaneously."



var tags = autoTagger



// using portuguese Brasil stop words

.useStopWords('pt-br')



// using english stop words

.useStopWords('en')



// adding aditionals stop words

.useStopWords(['will'])



// extract tags from text

// return tags that have at least 2 ocurrences

// and look for ocurrences of 4 consecutive words 

.fromText(testText,2,4)



// Format the output

.map(function(w){return w.word + ':'+ w.count}).join('\r\n');



/*

 * print results to the console:

 * "bayesian:5

 * bayesian inference:3

 * inference:3

 * techniques:3

 * techniques since:2

 * applications:2

 * since:2

 * models:2

 * allow:2"

 *

 */

console.log(tags);

Usage

html

or use it in Node.js:

js
$> npm install auto-tagger

js
var autoTagger = require('auto-tagger');

You can use the folowing methods to extract relevant tags from documents:

js
var testText = "This text is from a Wikipedia entry about Bayes' Theorem. Bayesian inference has applications in artificial intelligence and expert systems. Bayesian inference techniques have been a fundamental part of computerized pattern recognition techniques since the late 1950s. There is also an ever growing connection between Bayesian methods and simulation-based Monte Carlo techniques since complex models cannot be processed in closed form by a Bayesian analysis, while the graphical model structure inherent to statistical models, may allow for efficient simulation algorithms like the Gibbs sampling and other Metropolis-Hastings algorithm schemes. Recently Bayesian inference has gained popularity amongst the phylogenetics community for these reasons; applications such as BEAST, MrBayes and P4 allow many demographic and evolutionary parameters to be estimated simultaneously."

var tags = autoTagger

// using portuguese Brasil stop words
.useStopWords('pt-br')

// using english stop words
.useStopWords('en')

// adding aditionals stop words
.useStopWords(['will'])

// extract tags from text
// return tags that have at least 2 ocurrences
// and look for ocurrences of 4 consecutive words
.fromText(testText,2,4)

// Format the output
.map(function(w){return w.word + ':'+ w.count}).join('\r\n');

/*
* print results to the console:
* "bayesian:5
* bayesian inference:3
* inference:3
* techniques:3
* techniques since:2
* applications:2
* since:2
* models:2
* allow:2"
*
*/
console.log(tags);