jsPCA

matrix, with each row containing the
  distribution over terms for a topic, with as many rows as
  there are topics in the model, and as many columns as
  there are terms in the vocabulary.

Dimension reduction via Jensen-Shannon Divergence &
Principal Components

Tools to create an interactive web-based visualization of a
    topic model that has been fit to a corpus of text data using
    Latent Dirichlet Allocation (LDA). Given the estimated parameters of
    the topic model, it computes various summary statistics as input to
    an interactive visualization built with D3.js that is accessed via
    a browser. The goal is to help users interpret the topics in their
    LDA topic model.

Carson Sievert

LDAvis

Interactive Visualization of Topic Models

jsPCA function

Dimension reduction via Jensen-Shannon Divergence & Principal Components

Tools to create an interactive web-based visualization of a
topic model that has been fit to a corpus of text data using
Latent Dirichlet Allocation (LDA). Given the estimated parameters of
the topic model, it computes various summary statistics as input to
an interactive visualization built with D3.js that is accessed via
a browser. The goal is to help users interpret the topics in their
LDA topic model.

jsPCA: Dimension reduction via Jensen-Shannon Divergence & Principal Components

Description

Usage

Arguments