plot_similarity {PsychWordVec}

Visualize cosine similarity of word pairs.

Description

Visualize cosine similarity of word pairs.

Usage

plot_similarity(
  data,
  words = NULL,
  pattern = NULL,
  words1 = NULL,
  words2 = NULL,
  label = "auto",
  value.color = NULL,
  value.percent = FALSE,
  order = c("original", "AOE", "FPC", "hclust", "alphabet"),
  hclust.method = c("complete", "ward", "ward.D", "ward.D2", "single", "average",
    "mcquitty", "median", "centroid"),
  hclust.n = NULL,
  hclust.color = "black",
  hclust.line = 2,
  file = NULL,
  width = 10,
  height = 6,
  dpi = 500,
  ...
)

Arguments

data

A wordvec (data.table) or embed (matrix), see data_wordvec_load.

words

[Option 1] Character string(s).

pattern

[Option 2] Regular expression (see str_subset). If neither words nor pattern are specified (i.e., both are NULL), then all words in the data will be extracted.

words1, words2

[Option 3] Two sets of words for only n1 * n2 word pairs. See examples.

label

Position of text labels. Defaults to "auto" (add labels if less than 20 words). Can be TRUE (left and top), FALSE (add no labels of words), or a character string (see the usage of tl.pos in corrplot.

value.color

Color of values added on the plot. Defaults to NULL (add no values).

value.percent

Whether to transform values into percentage style for space saving. Defaults to FALSE.

order

Character, the ordering method of the correlation matrix.

• 'original' for original order (default).

• 'AOE' for the angular order of the eigenvectors.

• 'FPC' for the first principal component order.

• 'hclust' for the hierarchical clustering order.

• 'alphabet' for alphabetical order.

See function corrMatOrder for details.

hclust.method

Character, the agglomeration method to be used when order is hclust. This should be one of 'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.

hclust.n

Number of rectangles to be drawn on the plot according to the hierarchical clusters, only valid when order="hclust". Defaults to NULL (add no rectangles).

hclust.color

Color of rectangle border, only valid when hclust.n >= 1. Defaults to "black".

hclust.line

Line width of rectangle border, only valid when hclust.n >= 1. Defaults to 2.

file

File name to be saved, should be png or pdf.

width, height

Width and height (in inches) for the saved file. Defaults to 10 and 6.

dpi

Dots per inch. Defaults to 500 (i.e., file resolution: 4000 * 3000).

...

Other parameters passed to corrplot.

Value

Invisibly return a matrix of cosine similarity between each pair of words.

Download

Download pre-trained word vectors data (.RData): https://psychbruce.github.io/WordVector_RData.pdf

See Also

cosine_similarity

pair_similarity

tab_similarity

most_similar

plot_network

Examples

w1 = cc("king, queen, man, woman")
plot_similarity(demodata, w1)
plot_similarity(demodata, w1,
                value.color="grey",
                value.percent=TRUE)
plot_similarity(demodata, w1,
                value.color="grey",
                order="hclust",
                hclust.n=2)

plot_similarity(
  demodata,
  words1=cc("man, woman, king, queen"),
  words2=cc("he, she, boy, girl, father, mother"),
  value.color="grey20"
)

w2 = cc("China, Chinese,
         Japan, Japanese,
         Korea, Korean,
         man, woman, boy, girl,
         good, bad, positive, negative")
plot_similarity(demodata, w2,
                order="hclust",
                hclust.n=3)
plot_similarity(demodata, w2,
                order="hclust",
                hclust.n=7,
                file="plot.png")

unlink("plot.png")  # delete file for code check