token.rank: Compute distinctiveness and saliency of the words in the...
In kshirley/LDAtools: Tools to fit a topic model using Latent Dirichlet Allocation (LDA)
Description
Usage
Arguments
Value
Description
This function computes 'distinctiveness' and 'saliency' as
defined in Chuang, et al (2012), 'Termite'.
Usage
1
token.rank(word.id = integer(), topic.id = integer(), phi.hat = numeric())
Arguments
word.id
an integer vector with the token id of
each token occurrence in the data.
topic.id
an integer vector with the topic id for
each token in the data.
phi.hat
a numeric (K x W) matrix with topic
distributions on each of the K rows
Value
A list of two length-W vectors, the distinctiveness and
saliency of each of the W tokens in the vocab
kshirley/LDAtools documentation built on May 20, 2019, 7:03 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value
Description
This function computes 'distinctiveness' and 'saliency' as defined in Chuang, et al (2012), 'Termite'.
Usage
1 | token.rank(word.id = integer(), topic.id = integer(), phi.hat = numeric())
|
Arguments
word.id |
an integer vector with the token id of each token occurrence in the data. |
topic.id |
an integer vector with the topic id for each token in the data. |
phi.hat |
a numeric (K x W) matrix with topic distributions on each of the K rows |
Value
A list of two length-W vectors, the distinctiveness and saliency of each of the W tokens in the vocab
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.