LDA

Latent Dirichlet allocation (LDA) topic modeling in javascript for node.js. LDA is a machine learning algorithm that extracts topics and their related keywords from a collection of documents.

In LDA, a document may contain several different topics, each with their own related terms. The algorithm uses a probabilistic model for detecting the number of topics specified and extracting their related keywords. For example, a document may contain topics that could be classified as beach-related and weather-related. The beach topic may contain related words, such as sand, ocean, and water. Similarly, the weather topic may contain related words, such as sun, temperature, and clouds.

See http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation

$ npm install lda

Usage

varlda=require('lda');// Example document.vartext='Cats are small. Dogs are big. Cats like to chase mice. Dogs like to eat bones.';// Extract sentences.vardocuments=text.match(/[^\.!\?]+[\.!\?]+/g);// Run LDA to get terms for 2 topics (5 terms each).varresult=lda(documents,2,5);

The above example produces the following result with two topics (topic 1 is "cat-related", topic 2 is "dog-related"):

Topic 1
cats (0.21%)
dogs (0.19%)
small (0.1%)
mice (0.1%)
chase (0.1%)
Topic 2
dogs (0.21%)
cats (0.19%)
big (0.11%)
eat (0.1%)
bones (0.1%)

Output

LDA returns an array of topics, each containing an array of terms. The result contains the following format:

[ [ { term: 'dogs', probability: 0.2 },
{ term: 'cats', probability: 0.2 },
{ term: 'small', probability: 0.1 },
{ term: 'mice', probability: 0.1 },
{ term: 'chase', probability: 0.1 } ],
[ { term: 'dogs', probability: 0.2 },
{ term: 'cats', probability: 0.2 },
{ term: 'bones', probability: 0.11 },
{ term: 'eat', probability: 0.1 },
{ term: 'big', probability: 0.099 } ] ]

The result can be traversed as follows:

varresult=lda(documents,2,5);// For each topic.for(variinresult){varrow=result[i];console.log('Topic '+(parseInt(i)+1));// For each term.for(varjinrow){varterm=row[j];console.log(term.term+' ('+term.probability+'%)');}console.log('');}

Additional Languages

LDA uses stop-words to ignore common terms in the text (for example: this, that, it, we). By default, the stop-words list uses English. To use additional languages, you can specify an array of language ids, as follows:

// Use English (this is the default).result=lda(documents,2,5,['en']);// Use German.result=lda(documents,2,5,['de']);// Use English + German.result=lda(documents,2,5,['en','de']);

To add a new language-specific stop-words list, create a file /lda/lib/stopwords_XX.js where XX is the id for the language. For example, a French stop-words list could be named "stopwords_fr.js". The contents of the file should follow the format of an existing stop-words list. The format is, as follows:

exports.stop_words=['cette','que','une','il'];

Setting a Random Seed

A specific random seed can be used to compute the same terms and probabilities during subsequent runs. You can specify the random seed, as follows:

// Use the random seed 123.result=lda(documents,2,5,null,null,null,123);

Author

Kory Becker http://www.primaryobjects.com

Based on original javascript implementation https://github.com/awaisathar/lda.js