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 0.2


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