View source: R/textplot_biterms.R
textplot_bitermclusters | R Documentation |
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.
textplot_bitermclusters(x, ...) ## Default S3 method: textplot_bitermclusters( x, biterms, which, labels = seq_len(length(table(biterms$topic))), title = "Biterm topic model", subtitle = list(), ... )
x |
a list of data.frames, each containing the columns token and probability corresponding to how good a token is emitted by a topic. The list index is assumed to be the topic number |
... |
not used |
biterms |
a data.frame with columns term1, term2, topic with all biterms and the topic these were assigned to |
which |
integer vector indicating to display only these topics. See the examples. |
labels |
a character vector of names. Should be of the same length as the number of topics in the data. |
title |
character string with the title to use in the plot |
subtitle |
character string with the subtitle to use in the plot |
an object of class ggplot
library(igraph) library(ggraph) library(concaveman) library(ggplot2) library(BTM) data(example_btm, package = 'textplot') group_terms <- terms(example_btm, top_n = 3) group_biterms <- example_btm$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)
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