JSTOR_lda

object returned by the function JSTOR_unpack1grams.

unpack1grams

the object returned by the function JSTOR_dtmtonouns.

nouns

the number of topics that the model should contain

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

alpha


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 (<a href="http://dfr.jstor.org/">http://dfr.jstor.org/</a>).


Simple exploratory text mining and document clustering of journal
articles from JSTOR's Data for Research service. Go to
\url{http://dfr.jstor.org/}, make a request for data (specify CSV as outout
format and Word Counts as data type), then once you get a zip file, unzip
it and start with one of the unpack functions and then you're ready to go
with any of the other functions. For more details on installation and
usage, see \url{https://github.com/benmarwick/JSTORr/}

Ben Marwick

JSTORr

Simple Text Mining and Document Clustering of JSTOR Journal Articles

JSTOR_lda function


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 (<a href = 'http://dfr.jstor.org/'>http://dfr.jstor.org/</a>).


JSTOR_lda: Generate a topic model with K topics using the implementation of Latent Dirichlet allocation (LDA) in the lda package

Description

Usage

Arguments

Value

Examples