https://github.com/jparkerweb/extract-topics
👽 Extract Topics ⇢ use LDA (Latent Dirichlet Allocation) to extract topics from text
https://github.com/jparkerweb/extract-topics
extraction lda nlp text topic topic-extraction
Last synced: 11 months ago
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👽 Extract Topics ⇢ use LDA (Latent Dirichlet Allocation) to extract topics from text
- Host: GitHub
- URL: https://github.com/jparkerweb/extract-topics
- Owner: jparkerweb
- Created: 2024-12-16T06:06:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-25T05:56:17.000Z (over 1 year ago)
- Last Synced: 2025-03-13T23:47:48.170Z (over 1 year ago)
- Topics: extraction, lda, nlp, text, topic, topic-extraction
- Language: JavaScript
- Homepage: https://www.npmjs.com/package/extract-topics
- Size: 170 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
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README
# 👽 Extract Topics
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
Install dependencies:
```bash
npm install extractTopics
```
## Usage
```bash
import { extractTopics } from 'extractTopics';
const result = await extractTopics(text, { numTopics, numTerms });
console.log(result);
```
## API
### topicExtraction(text, options)
Extracts topics from input text using LDA.
#### Parameters
- `text` (string): The input text to analyze
- `options` (object):
- `numTopics` (number, optional): Number of topics to extract. Default: 2
- `numTerms` (number, optional): Number of terms per topic. Default: 5
#### Returns
Returns a Promise that resolves to the LDA analysis result.
### Example script
```bash
npm run example
```
The example will:
1. Load sample text documents
2. Apply LDA to extract the main topics
3. Output the discovered topics and their key terms
## About LDA
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.
---
#### Project reference
- https://www.npmjs.com/package/ldawithmorelanguages