topics

topics,TopicModel-method

terms,TopicModel-method

get_terms

get_topics

Function to extract the most likely terms for each topic or the most
 likely topics for each document.

utilities

terms_and_topics

Provides an interface to the C code for Latent Dirichlet
Allocation (LDA) models and Correlated Topics Models
(CTM) by David M. Blei and co-authors and the C++ code
for fitting LDA models using Gibbs sampling by Xuan-Hieu
Phan and co-authors.

Bettina Gruen

topicmodels

Topic Models

Bettina Grün

Kurt Hornik

David M Blei

John D Lafferty

Xuan-Hieu Phan

Makoto Matsumoto

Takuji Nishimura

Shawn Cokus

terms_and_topics function

<dl> <dt>x</dt>
<dd>Object of class <code>"TopicModel"</code>.</dd> <dt>k</dt>
<dd>The maximum number of terms/topics returned. By default
 set to 1 if no threshold is given.</dd> <dt>threshold</dt>
<dd>Only the terms/topics which are more likely than
 the threshold are returned.</dd> <dt>...</dt>
<dd>Further arguments passed to <code>sapply</code>.</dd></dl>

Arguments

Author

Extract most likely terms or topics. — terms_and_topics

<dl>

 <dt>x</dt>
<dd>Object of class <code>"TopicModel"</code>.</dd>

 <dt>k</dt>
<dd>The maximum number of terms/topics returned. By default
 set to 1 if no threshold is given.</dd>

 <dt>threshold</dt>
<dd>Only the terms/topics which are more likely than
 the threshold are returned.</dd>

 <dt>...</dt>
<dd>Further arguments passed to <code>sapply</code>.</dd>

</dl>

Extract most likely terms or topics.

terms_and_topics: Extract most likely terms or topics.

Description

Usage

Value

Arguments

Author

See Also