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ldawithmorelanguages

v0.0.1

Published

LDA topic modeling for node.js with more languages.

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Links

Git

Maintainers

herasiumherasium

Keywords

ldaLatent Dirichlet allocationLatent DirichletDirichletmachine learningmlartificial intelligencenatural language processingnatural languagetopic modeltopic modelingtopic modellingnlptopicsdocumentwords

Readme

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

Author

Kory Becker http://www.primaryobjects.com

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

Added stop-words for a lot of languages https://github.com/stopwords-iso/stopwords-iso