CalcProbCoherence: Probabilistic coherence of topics

In TommyJones/textmineR: Functions for Text Mining and Topic Modeling

Probabilistic coherence of topics

Description

Calculates the probabilistic coherence of a topic or topics. This approximates semantic coherence or human understandability of a topic.

Usage

CalcProbCoherence(phi, dtm, M = 5)

Arguments

phi	A numeric matrix or a numeric vector. The vector, or rows of the matrix represent the numeric relationship between topic(s) and terms. For example, this relationship may be p(word|topic) or p(topic|word).
dtm	A document term matrix or co-occurrence matrix of class matrix or whose class inherits from the Matrix package. Columns must index terms.
M	An integer for the number of words to be used in the calculation. Defaults to 5

Value

Returns an object of class numeric corresponding to the probabilistic coherence of the input topic(s).

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_topic_model)
data(nih_sample_dtm) 

CalcProbCoherence(phi = nih_sample_topic_model$phi, dtm = nih_sample_dtm, M = 5)

TommyJones/textmineR documentation built on July 26, 2023, 9:51 p.m.