getTopicLabel: Label LDA topics
In Chrisss93/cnn.stories: Utilities for CNN-stories project
Description
Usage
Arguments
Value
Description
Use vector algebra on word embeddings of most relevant words in topics to derive single topic label
Usage
1
getTopicLabel(lambda = 0.3, lda, word_vector)
Arguments
lambda
A numeric from 0 to 1. This determines the relevancy score
lda
A pre-trained LatentDirichletAllocation model.
word_vector
A numeric matrix. Column-bound word vectors.
Value
A character vector
Chrisss93/cnn.stories documentation built on May 23, 2019, 7:31 a.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
Use vector algebra on word embeddings of most relevant words in topics to derive single topic label
Usage
1 | getTopicLabel(lambda = 0.3, lda, word_vector)
|
Arguments
lambda |
A numeric from 0 to 1. This determines the relevancy score |
lda |
A pre-trained LatentDirichletAllocation model. |
word_vector |
A numeric matrix. Column-bound word vectors. |
Value
A character vector
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.