npm package: @stdlib/nlp-lda
Description: Latent Dirichlet Allocation via collapsed Gibbs sampling.
Author: The Stdlib Authors
Latest version: 0.2.2

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  <summary>
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# LDA

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

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

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/nlp-lda
```

</section>

<section class="usage">

## 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 )

<!-- run-disable -->

```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`.

<!-- run-disable -->

```javascript
var words = model.getTerms( 0, 3 );
/* returns
    [
        { 'word': 'both', 'prob': 0.06315008476532499 },
        { 'word': 'pain', 'prob': 0.05515729517235543 },
        { 'word': 'one', 'prob': 0.05486669737616135 }
    ]
*/
```

</section>

<!-- /.usage -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```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( ', ' ) );
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->


<section class="main-repo" >

* * *

## 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 &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

<!-- /.stdlib -->

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<section class="links">

[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=v0.2.2
[test-url]: https://github.com/stdlib-js/nlp-lda/actions/workflows/test.yml?query=branch:v0.2.2

[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

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[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

</section>

<!-- /.links -->