runShiny

a matrix with W rows, one for each term in the
  vocabulary, and K columns, one for each topic, where each
  column sums to one. Each column is the multinomial
  distribution over terms for a given topic in an LDA topic
  model.

an integer vector of length W
  containing the frequency of each term in the vocabulary.

term.frequency

a character vector of length W containing
  the unique terms in the corpus.

vocab

a numeric vector of length K
  containing the proportion of each topic in the corpus.

topic.proportion

This function is deprecated as of version 10.2

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.

runShiny: Run shiny/D3 visualization

Description

Usage

Arguments