textplot_bitermclusters {textplot}

Plot biterm cluster groups

Description

Plot biterms as a clustered graph. The graph is constructed by assigning each word to a topic and within a topic of words biterm frequencies are shown.

Usage

textplot_bitermclusters(x, ...)

## Default S3 method:
textplot_bitermclusters(
  x,
  biterms,
  which,
  labels = seq_len(length(table(biterms$topic))),
  title = "Biterm topic model",
  subtitle = list(),
  ...
)

Arguments

x

a list of data.frames, each containing the columns token and probability corresponding to how good a token is emitted by a topic. The list index is assumed to be the topic number

...

not used

biterms

a data.frame with columns term1, term2, topic with all biterms and the topic these were assigned to

which

integer vector indicating to display only these topics. See the examples.

labels

a character vector of names. Should be of the same length as the number of topics in the data.

title

character string with the title to use in the plot

subtitle

character string with the subtitle to use in the plot

Value

an object of class ggplot

Examples

library(igraph)
library(ggraph)
library(concaveman)
library(ggplot2)
library(BTM)
data(example_btm, package = 'textplot')
group_terms   <- terms(example_btm, top_n = 3)
group_biterms <- example_btm$biterms$biterms


textplot_bitermclusters(x = group_terms, biterms = group_biterms)
textplot_bitermclusters(x = group_terms, biterms = group_biterms,
                        title = "BTM model", subtitle = "Topics 7-15",
                        which = 7:15, labels = seq_len(example_btm$K))

group_terms   <- terms(example_btm, top_n = 10)
textplot_bitermclusters(x = group_terms, biterms = group_biterms,
                        title = "BTM model", subtitle = "Topics 1-5",
                        which = 1:5, labels = seq_len(example_btm$K))

group_terms   <- terms(example_btm, top_n = 7)
topiclabels <- c("Garbage",
  "Data Mining", "Gradient descent", "API's",
  "Random Forests", "Stat models", "Text Mining / NLP",
  "GLM / GAM / Bayesian", "Machine learning", "Variable selection",
  "Regularisation techniques", "Optimisation", "Fuzzy logic",
  "Classification/Regression trees", "Text frequencies",
  "Neural / Deep learning", "Variable selection",
  "Text file handling", "Text matching", "Topic modelling")
textplot_bitermclusters(x = group_terms, biterms = group_biterms,
                        title = "Biterm topic model", subtitle = "some topics",
                        which = c(3, 4, 5, 6, 7, 9, 12, 16, 20),
                        labels = topiclabels)