JSTOR_lda: Generate a topic model with K topics using the implementation...

In benmarwick/JSTORr: Simple Text Mining and Document Clustering of JSTOR Journal Articles

Description

Generates a topic model with K topics using Latent Dirichlet allocation (LDA, with the lda package) For use with JSTOR's Data for Research datasets (http://dfr.jstor.org/).

Usage

JSTOR_lda(unpack1grams, nouns, K, alpha = 50/K)

Arguments

unpack1grams	object returned by the function JSTOR_unpack1grams.
nouns	the object returned by the function JSTOR_dtmtonouns.
K	the number of topics that the model should contain
alpha	The scalar value of the Dirichlet hyperparameter for topic proportions. Higher values lead to more uniform distributions of topics over documents. Default is 50/K

Value

Returns a data frame with documents as rows, topics as columns and posterior probabilities as cell values.

Examples

## lda1 <- JSTOR_lda(unpack1grams, nouns, K = 150) 

benmarwick/JSTORr documentation built on May 12, 2019, 12:59 p.m.