get_terms: Get Terms Based on Cluster Assignment in 'assign_cluster'

In trinker/hclustext: Optimized Hierarchical Clustering for Text Data

Description

Get the terms weighted and scaled by tf-idf + min/max scaling associated with each of the k clusters .

Usage

get_terms(x, term.cutoff = 0.1, min.n = 2, nrow = NULL, ...)

## S3 method for class 'assign_cluster'
get_terms(x, term.cutoff = 0.1, min.n = 2,
  nrow = NULL, ...)

Arguments

x	A assign_cluster object.
term.cutoff	The lowest min/max scaled tf-idf weighting to consider as a document's salient term.
min.n	The minimum number of terms a term must appear in a topic to be displayed in the returned data.frames.
nrow	The max number of rows to display in the returned data.frames.
...	ignored.

Value

Returns a list of data.frames of top weighted terms.

Examples

library(dplyr)
library(textshape)

myterms <- presidential_debates_2012 %>%
    with(data_store(dialogue)) %>%
    hierarchical_cluster() %>%
    assign_cluster(k = 55) %>%
    get_terms()

myterms
textshape::bind_list(myterms[!sapply(myterms, is.null)], "Topic")
## Not run: 
library(ggplot2)
library(gridExtra)
library(dplyr)
library(textshape)
library(wordcloud)

max.n <- max(textshape::bind_list(myterms)[["n"]])

myplots <- Map(function(x, y){
    x %>%
        mutate(term = factor(term, levels = rev(term))) %>%
        ggplot(aes(term, weight=n)) +
            geom_bar() +
            scale_y_continuous(expand = c(0, 0),limits=c(0, max.n)) +
            ggtitle(sprintf("Topic: %s", y)) +
            coord_flip()
}, myterms, names(myterms))

myplots[["ncol"]] <- 10

do.call(gridExtra::grid.arrange, myplots[!sapply(myplots, is.null)])

##wordclouds
par(mfrow=c(5, 11), mar=c(0, 4, 0, 0))
Map(function(x, y){
    wordcloud::wordcloud(x[[1]], x[[2]], scale=c(1,.25),min.freq=1)
    mtext(sprintf("Topic: %s", y), col = "blue", cex=.55, padj = 1.5)
}, myterms, names(myterms))

## End(Not run)

trinker/hclustext documentation built on May 31, 2019, 8:50 p.m.