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https://github.com/stdlib-js/nlp-lda

Latent Dirichlet Allocation via collapsed Gibbs sampling.
https://github.com/stdlib-js/nlp-lda

bayesian clustering javascript learning mcmc mixed-membership model nlp node node-js nodejs stdlib text-mining topic unsupervised

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Latent Dirichlet Allocation via collapsed Gibbs sampling.

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README 

        



  

    About stdlib...

  

  
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To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!



# LDA



[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] 



> [Latent Dirichlet Allocation][lda] via collapsed Gibbs sampling.



## Installation



```bash

npm install @stdlib/nlp-lda

```



Alternatively,



-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).

-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).

-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).



The [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.



To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.



## Usage



```javascript

var lda = require( '@stdlib/nlp-lda' );

```



#### lda( docs, K\[, options] )



[Latent Dirichlet Allocation][lda] via collapsed Gibbs sampling. To create a model, call the `lda` function by passing it an `array` of `strings` and the number of topics `K` that should be identified.



```javascript

var model;

var docs;



docs = [

    'I loved you first',

    'For one is both and both are one in love',

    'You never see my pain',

    'My love is such that rivers cannot quench',

    'See a lot of pain, a lot of tears'

];



model = lda( docs, 2 );

// returns {}

```



After initialization, model parameters are estimated by calling the `.fit()` method, which performs collapsed Gibbs sampling.



The model object contains the following methods:



#### model.fit( iter, burnin, thin )



```javascript

model.fit( 1000, 100, 10 );

```



The `iter` parameter denotes the number of sampling iterations. While a common choice, one thousand iterations might not always be appropriate. Empirical diagnostics can be used to assess whether the constructed Markov Chain has converged. `burnin` denotes the number of estimates that are thrown away at the beginning, whereas `thin` controls the number of estimates discarded in-between iterations.



#### model.getTerms( k\[, no = 10] )



Returns the `no` terms with the highest probabilities for chosen topic `k`.



```javascript

var words = model.getTerms( 0, 3 );

/* returns

    [

        { 'word': 'both', 'prob': 0.06315008476532499 },

        { 'word': 'pain', 'prob': 0.05515729517235543 },

        { 'word': 'one', 'prob': 0.05486669737616135 }

    ]

*/

```



## Examples



```javascript

var sotu = require( '@stdlib/datasets-sotu' );

var roundn = require( '@stdlib/math-base-special-roundn' );

var stopwords = require( '@stdlib/datasets-stopwords-en' );

var lowercase = require( '@stdlib/string-lowercase' );

var lda = require( '@stdlib/nlp-lda' );



var speeches;

var words;

var terms;

var model;

var str;

var i;

var j;



words = stopwords();

for ( i = 0; i < words.length; i++ ) {

    words[ i ] = new RegExp( '\\b'+words[ i ]+'\\b', 'gi' );

}



speeches = sotu({

    'range': [ 1930, 2010 ]

});

for ( i = 0; i < speeches.length; i++ ) {

    str = lowercase( speeches[ i ].text );

    for ( j = 0; j < words.length; j++ ) {

        str = str.replace( words[ j ], '' );

    }

    speeches[ i ] = str;

}



model = lda( speeches, 3 );



model.fit( 1000, 100, 10 );



for ( i = 0; i <= 80; i++ ) {

    str = 'Year: ' + (1930+i) + '\t';

    str += 'Topic 1: ' + roundn( model.avgTheta.get( i, 0 ), -3 ) + '\t';

    str += 'Topic 2: ' + roundn( model.avgTheta.get( i, 1 ), -3 ) + '\t';

    str += 'Topic 3: ' + roundn( model.avgTheta.get( i, 2 ), -3 );

    console.log( str );

}



terms = model.getTerms( 0, 20 );

for ( i = 0; i < terms.length; i++ ) {

    terms[ i ] = terms[ i ].word;

}

console.log( 'Words most associated with first topic:\n ' + terms.join( ', ' ) );



terms = model.getTerms( 1, 20 );

for ( i = 0; i < terms.length; i++ ) {

    terms[ i ] = terms[ i ].word;

}

console.log( 'Words most associated with second topic:\n ' + terms.join( ', ' ) );



terms = model.getTerms( 2, 20 );

for ( i = 0; i < terms.length; i++ ) {

    terms[ i ] = terms[ i ].word;

}

console.log( 'Words most associated with third topic:\n ' + terms.join( ', ' ) );

```



* * *



## Notice



This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.



For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].



#### Community



[![Chat][chat-image]][chat-url]



---



## License



See [LICENSE][stdlib-license].



## Copyright



Copyright © 2016-2024. The Stdlib [Authors][stdlib-authors].



[npm-image]: http://img.shields.io/npm/v/@stdlib/nlp-lda.svg

[npm-url]: https://npmjs.org/package/@stdlib/nlp-lda



[test-image]: https://github.com/stdlib-js/nlp-lda/actions/workflows/test.yml/badge.svg?branch=main

[test-url]: https://github.com/stdlib-js/nlp-lda/actions/workflows/test.yml?query=branch:main



[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/nlp-lda/main.svg

[coverage-url]: https://codecov.io/github/stdlib-js/nlp-lda?branch=main



[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg

[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im



[stdlib]: https://github.com/stdlib-js/stdlib



[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors



[umd]: https://github.com/umdjs/umd

[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules



[deno-url]: https://github.com/stdlib-js/nlp-lda/tree/deno

[deno-readme]: https://github.com/stdlib-js/nlp-lda/blob/deno/README.md

[umd-url]: https://github.com/stdlib-js/nlp-lda/tree/umd

[umd-readme]: https://github.com/stdlib-js/nlp-lda/blob/umd/README.md

[esm-url]: https://github.com/stdlib-js/nlp-lda/tree/esm

[esm-readme]: https://github.com/stdlib-js/nlp-lda/blob/esm/README.md

[branches-url]: https://github.com/stdlib-js/nlp-lda/blob/main/branches.md



[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/nlp-lda/main/LICENSE



[lda]: https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation