labelTopics: Label topics

In stm: Estimation of the Structural Topic Model

Label topics

Description

Generate a set of words describing each topic from a fitted STM object. Uses a variety of labeling algorithms (see details).

Usage

labelTopics(model, topics = NULL, n = 7, frexweight = 0.5)

Arguments

model	An STM model object.
topics	A vector of numbers indicating the topics to include. Default is all topics.
n	The desired number of words (per type) used to label each topic. Must be 1 or greater.
frexweight	A weight used in our approximate FREX scoring algorithm (see details).

Details

Four different types of word weightings are printed with label topics.

Highest Prob: are the words within each topic with the highest probability (inferred directly from topic-word distribution parameter \beta).

FREX: are the words that are both frequent and exclusive, identifying words that distinguish topics. This is calculated by taking the harmonic mean of rank by probability within the topic (frequency) and rank by distribution of topic given word p(z|w=v) (exclusivity). In estimating exclusivity we use a James-Stein type shrinkage estimator of the distribution p(z|w=v). More information can be found in the documentation for the internal function calcfrex and js.estimate.

Score and Lift are measures provided in two other popular text mining packages. For more information on type Score, see the R package lda or the internal function calcscore. For more information on type Lift, see the R package maptpx or or the internal function calclift.

Value

A labelTopics object (list)

prob	matrix of highest probability words
frex	matrix of highest ranking frex words
lift	matrix of highest scoring words by lift
score	matrix of best words by score
topicnums	a vector of topic numbers which correspond to the rows

See Also

stm plot.STM calcfrex js.estimate calcscore calclift

Examples

labelTopics(gadarianFit)

stm documentation built on June 24, 2024, 5:18 p.m.