topic_docs_word: The topic-document matrix for a specific word
In agoldst/dfrtopics: Tools for exploring topic models of text
topic_docs_word R Documentation
The topic-document matrix for a specific word
Description
Extracts a matrix of counts of a word's weight in each topic within each
document from the model's final Gibbs sampling state. (The matrix is quite
sparse.)
Usage
topic_docs_word(m, word)
Arguments
m
a mallet_model object with the sampling state loaded
read_sampling_state. Operated on using
mwhich.
Details
This is useful for studying a word's distribution over topics conditional on
some metadata covariate. It is important to realize that the model does not
distribute the word among topics uniformly across the corpus.
Value
a sparseMatrix of within-document word
weights for word (columns are in doc_ids(m) order)
See Also
tdm_topic, read_sampling_state,
mallet_model, load_sampling_state,
top_n_row, sum_col_groups
agoldst/dfrtopics documentation built on July 15, 2022, 4:13 p.m.
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| topic_docs_word | R Documentation |
The topic-document matrix for a specific word
Description
Extracts a matrix of counts of a word's weight in each topic within each document from the model's final Gibbs sampling state. (The matrix is quite sparse.)
Usage
topic_docs_word(m, word)
Arguments
m |
a |
Details
This is useful for studying a word's distribution over topics conditional on some metadata covariate. It is important to realize that the model does not distribute the word among topics uniformly across the corpus.
Value
a sparseMatrix of within-document word
weights for word (columns are in doc_ids(m) order)
See Also
tdm_topic, read_sampling_state,
mallet_model, load_sampling_state,
top_n_row, sum_col_groups
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.
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