newsgroups: Topic modeling results from the "20 Newsgroups" data set.

In stephenslab/fastTopics: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

Topic modeling results from the “20 Newsgroups” data set.

Description

These are topic modeling results from the “20 Newsgroups” data, with k = 10 topics. The data were originally downloaded from http://qwone.com/~jason/20Newsgroups and prepared by running code that found in an R Markdown file in this GitHub repository: https://github.com/stephenslab/fastTopics-experiments. See the “inst” directory of this package for the scripts used to generate these results.

Format

newsgroups is a list with the following elements:

topics

Original labeling of the documents: each document is from one of 20 “newsgroups”.

L

Estimated topic proportions matrix; rows are documents and columns are topics.

F

Matrix containing posterior mean estimates of log-fold changes (in base-2 logarithm). These were computed using de_analysis with lfc.stat = "vsnull". Columns are words and columns are topics.

Examples

data(newsgroups)
table(newsgroups$topics)
dim(newsgroups$L)
dim(newsgroups$F)

stephenslab/fastTopics documentation built on March 29, 2025, 3:24 p.m.