tf_df_dist_diff

Helper function to calculate the Hellinger distance
between the token frequencies and document frequencies
for a specific topic's top N tokens

Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for
Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package.
For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080),
pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.

Doug Friedman

topicdoc

Topic-Specific Diagnostics for LDA and CTM Topic Models

tf_df_dist_diff function

<dl><dt>dtm_data</dt>
<dd>a document-term matrix of token counts coercible to <code>simple_triplet_matrix</code></dd>
<dt>top_terms</dt>
<dd>- a character vector of the top N tokens</dd></dl>

Arguments

Helper function to calculate the Hellinger distance
between the token frequencies and document frequencies
for a specific topic's top N tokens — tf_df_dist_diff

<dl>

<dt>dtm_data</dt>
<dd>a document-term matrix of token counts coercible to <code>simple_triplet_matrix</code></dd>


<dt>top_terms</dt>
<dd>- a character vector of the top N tokens</dd>

</dl>

tf_df_dist_diff: Helper function to calculate the Hellinger distance between the token frequencies and document frequencies for a specific topic's top N tokens

Description

Usage

Value

Arguments