Use LDA (Latent Dirichlet Allocation) to extract topics from text
Simple NPM package for using Latent Dirichlet Allocation (LDA) for topic modeling on text inputs.
Install dependencies:
npm install extractTopicsimport { extractTopics } from 'extractTopics';
const result = await extractTopics(text, { numTopics, numTerms });
console.log(result);Extracts topics from input text using LDA.
text(string): The input text to analyzeoptions(object):numTopics(number, optional): Number of topics to extract. Default: 2numTerms(number, optional): Number of terms per topic. Default: 5
Returns a Promise that resolves to the LDA analysis result.
npm run exampleThe example will:
- Load sample text documents
- Apply LDA to extract the main topics
- Output the discovered topics and their key terms
LDA is an unsupervised learning method that discovers topics in text documents. It views documents as random mixtures over latent topics, where each topic is characterized by a distribution over words.
