textCentralityPlot: Plot words according to semantic similarity to the aggregated...

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

Plot words according to semantic similarity to the aggregated word embedding.

Description

Plot words according to semantic similarity to the aggregated word embedding.

Usage

textCentralityPlot(
  word_data,
  min_freq_words_test = 1,
  plot_n_word_extreme = 10,
  plot_n_word_frequency = 10,
  plot_n_words_middle = 10,
  titles_color = "#61605e",
  x_axes = "central_semantic_similarity",
  title_top = "Semantic Centrality Plot",
  x_axes_label = "Semantic Centrality",
  scale_x_axes_lim = NULL,
  scale_y_axes_lim = NULL,
  word_font = NULL,
  centrality_color_codes = c("#EAEAEA", "#85DB8E", "#398CF9", "#9e9d9d"),
  word_size_range = c(3, 8),
  position_jitter_hight = 0,
  position_jitter_width = 0.03,
  point_size = 0.5,
  arrow_transparency = 0.1,
  points_without_words_size = 0.5,
  points_without_words_alpha = 0.5,
  legend_title = "SC",
  legend_x_axes_label = "x",
  legend_x_position = 0.02,
  legend_y_position = 0.02,
  legend_h_size = 0.2,
  legend_w_size = 0.2,
  legend_title_size = 7,
  legend_number_size = 2,
  seed = 1007
)

Arguments

word_data	Tibble from textPlotData.
min_freq_words_test	Select words to significance test that have occurred at least min_freq_words_test (default = 1).
plot_n_word_extreme	Number of words per dimension to plot with extreme Supervised Dimension Projection value. (i.e., even if not significant; duplicates are removed).
plot_n_word_frequency	Number of words to plot according to their frequency. (i.e., even if not significant).
plot_n_words_middle	Number of words to plot that are in the middle in Supervised Dimension Projection score (i.e., even if not significant; duplicates are removed).
titles_color	Color for all the titles (default: "#61605e").
x_axes	Variable to be plotted on the x-axes (default is "central_semantic_similarity", could also select "n", "n_percent").
title_top	Title (default " ").
x_axes_label	Label on the x-axes.
scale_x_axes_lim	Length of the x-axes (default: NULL, which uses c(min(word_data$central_semantic_similarity)-0.05, max(word_data$central_semantic_similarity)+0.05); change this by e.g., try c(-5, 5)).
scale_y_axes_lim	Length of the y-axes (default: NULL, which uses c(-1, 1); change e.g., by trying c(-5, 5)).
word_font	Type of font (default: NULL).
centrality_color_codes	Colors of the words selected as plot_n_word_extreme (minimum values), plot_n_words_middle, plot_n_word_extreme (maximum values) and plot_n_word_frequency; the default is c("#EAEAEA", "#85DB8E", "#398CF9", "#9e9d9d", respectively.
word_size_range	Vector with minimum and maximum font size (default: c(3, 8)).
position_jitter_hight	Jitter height (default: .0).
position_jitter_width	Jitter width (default: .03).
point_size	Size of the points indicating the words' position (default: 0.5).
arrow_transparency	Transparency of the lines between each word and point (default: 0.1).
points_without_words_size	Size of the points not linked to a word (default is to not show the point; , i.e., 0).
points_without_words_alpha	Transparency of the points that are not linked to a word (default is to not show it; i.e., 0).
legend_title	Title of the color legend (default: "(SCP)").
legend_x_axes_label	Label on the color legend (default: "(x)".
legend_x_position	Position on the x coordinates of the color legend (default: 0.02).
legend_y_position	Position on the y coordinates of the color legend (default: 0.05).
legend_h_size	Height of the color legend (default 0.15).
legend_w_size	Width of the color legend (default 0.15).
legend_title_size	Font size of the title (default = 7).
legend_number_size	Font size of the values in the legend (default = 2).
seed	Set different seed.

Value

A 1-dimensional word plot based on similarity to the aggregated word embedding, as well as tibble with processed data used to plot.

See Also

see textCentrality and textProjection

Examples

# The test-data included in the package is called: centrality_data_harmony
names(centrality_data_harmony)
# Plot
# centrality_plot <- textCentralityPlot(
#  word_data = centrality_data_harmony,
#  min_freq_words_test = 10,
#  plot_n_word_extreme = 10,
#  plot_n_word_frequency = 10,
#  plot_n_words_middle = 10,
#  titles_color = "#61605e",
#  x_axes = "central_semantic_similarity",
#
#  title_top = "Semantic Centrality Plot",
#  x_axes_label = "Semantic Centrality",
#
#  word_font = NULL,
#  centrality_color_codes = c("#EAEAEA", "#85DB8E", "#398CF9", "#9e9d9d"),
#  word_size_range = c(3, 8),
#  point_size = 0.5,
#  arrow_transparency = 0.1,
#  points_without_words_size = 0.5,
#  points_without_words_alpha = 0.5,
# )
# centrality_plot

text documentation built on Aug. 9, 2023, 5:08 p.m.