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Lda

Lda is an open-source JavaScript library for Node.js that applies Latent Dirichlet Allocation to document collections and returns topic groupings with associated terms and probabilities.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Lda — OpenViST project profile in AI Data & Analytics.
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Understand the project

Practical overview

When it fits

Use Lda when a Node.js application needs topic groupings and representative terms from a text collection, rather than only a list of keywords detached from inferred topics.

Who it is for

  • Node.js developers adding topic discovery to document-analysis workflows
  • Developers building text tools that need configurable topic and term extraction
  • Teams that want to inspect likely themes across collections of sentences or documents

Documented uses

  • Group documents by inferred topics and inspect representative terms.
  • Configure the number of topics and terms returned from a collection.
  • Filter common words with English or German stop-word lists.
  • Use a random seed to reproduce terms and probabilities across subsequent runs.
Before you choose

Requirements & limits

  • The library accepts a document collection and parameters for the number of topics and terms.
  • English stop words are the default; the documented options include English, German, or both.
  • A random seed can be supplied to compute the same terms and probabilities on subsequent runs.
  • Adding another language requires creating a stop-word file in the library’s expected format.
  • The repository lists an Apache-2.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Analyze a document collection for topics

Use the documented Node.js interface to obtain topic terms and probabilities from a collection of sentences.

Before you start

  • Node.js project using JavaScript
  • npm
  • A document collection, such as sentences extracted from text

Steps

  1. Install the package with the command below.

    Documented command
    npm install lda

  2. Import it in JavaScript with var lda = require('lda');.

  3. Pass the documents, requested topic count, and terms-per-topic count to lda, for example lda(documents, 2, 5).

  4. Traverse the returned array of topics and read each term and its probability.

Expected outcome

An array of topic groups, each containing terms and associated probabilities.

Guide limitations

  • The documentation’s example uses English stop words by default; language options are available for English and German.
  • The documented result provides probabilistic term groupings, not predefined topic labels.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
primaryobjects/lda · Official homepage title · retained in its source language
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