clusterTopics: Cluster Analysis

In tosca: Tools for Statistical Content Analysis

Description

This function makes a cluster analysis using the Hellinger distance.

Usage

clusterTopics(
  ldaresult,
  file,
  tnames = NULL,
  method = "average",
  width = 30,
  height = 15,
  ...
)

Arguments

ldaresult	The result of a function call LDAgen - alternatively the corresponding matrix result$topics
file	File for the dendogram pdf.
tnames	Character vector as label for the topics.
method	Method statement from hclust
width	Grafical parameter for pdf output. See pdf
height	Grafical parameter for pdf output. See pdf
...	Additional parameter for plot

Details

This function is useful to analyze topic similarities and while evaluating the right number of topics of LDAs.

Value

A dendogram as pdf and a list containing

dist	A distance matrix
clust	The result from hclust

Examples

texts <- list(A="Give a Man a Fish, and You Feed Him for a Day.
Teach a Man To Fish, and You Feed Him for a Lifetime",
B="So Long, and Thanks for All the Fish",
C="A very able manipulative mathematician, Fisher enjoys a real mastery
in evaluating complicated multiple integrals.")

corpus <- textmeta(meta=data.frame(id=c("A", "B", "C", "D"),
title=c("Fishing", "Don't panic!", "Sir Ronald", "Berlin"),
date=c("1885-01-02", "1979-03-04", "1951-05-06", "1967-06-02"),
additionalVariable=1:4, stringsAsFactors=FALSE), text=texts)

corpus <- cleanTexts(corpus)
wordlist <- makeWordlist(corpus$text)
ldaPrep <- LDAprep(text=corpus$text, vocab=wordlist$words)

LDA <- LDAgen(documents=ldaPrep, K = 3L, vocab=wordlist$words, num.words=3)
clusterTopics(ldaresult=LDA)

tosca documentation built on Oct. 28, 2021, 5:07 p.m.