Corpus plots¶

ruts.visualizers.dispersion_plot(), ruts.visualizers.keyness_plot(), ruts.visualizers.collocation_network()

Description¶

Plots for the corpus measures: lexical dispersion - where in the text a word occurs, a keyness chart from the results of keyness and a collocation network from the results of collocations. The matplotlib functions take axes ax and return Axes: without ax a new figure is created, with ax the plot goes into the user's grid; the collocation network is built by graphviz and returns a Graph, like the word tree.

Lexical dispersion¶

A row for every word of targets, a tick at the position of each of its occurrences in the text - like dispersion_plot in NLTK and textplot_xray in quanteda. Words are compared as they are: case and lemmatization are up to WordsExtractor, lemma search - via use_lexemes=True.

Parameter	Type	Default	Description
`words`	list[str]	`-`	Words of the text in order
`targets`	list[str]	`-`	Words whose occurrences to show
`ax`	Axes	`None`	Axes for the plot

Keyness chart¶

Diverging horizontal bars (quanteda textplot_keyness): words from positive to the right, from negative (the result of keyness with positive=False) to the left, bar length is the absolute value of the field (score, g2, log_ratio), so the side is set by the list, not by the sign of the measure; top_n words on each side, words with an undefined or infinite value are skipped. For the odds ratio (score from 0 to infinity, one means equal odds) set log=True: the absolute \(\log_2\) of the value is plotted, symmetric around one.

Parameter	Type	Default	Description
`positive`	list[Keyword]	`-`	Positive keywords
`negative`	list[Keyword]	`()`	Negative keywords
`top_n`	int	`20`	Number of words on each side
`labels`	tuple[str, str]	`("целевой корпус", "эталонный корпус")`	Legend labels
`field`	str	`score`	`Keyword` field whose values are plotted
`log`	bool	`False`	Plot \(\log_2\) of the value - for the odds ratio
`ax`	Axes	`None`	Axes for the plot

Collocation network¶

An undirected graph (quanteda textplot_network): nodes are words with font size by frequency, edges are pairs with width by the measure value and a label; neato layout. Rendering requires the Graphviz executables; in Jupyter the graph displays itself, graph.render("network", format="png") saves a file.

Parameter	Type	Default	Description
`collocations`	list[Collocation]	`-`	Collocations
`top_n`	int	`None`	Number of pairs from the start of the list; `None` - all

Usage example¶

Let us look at the visualizers on 12 texts of the StalinWorks dataset.

Example

Code:

# Import the libraries
import matplotlib.pyplot as plt
from ruts import WordsExtractor
from ruts.corpus import collocations, keyness
from ruts.datasets import FreqDict, StalinWorks
from ruts.visualizers import collocation_network, dispersion_plot, keyness_plot

# Prepare the data
sw = StalinWorks()
texts = list(sw.get_texts(limit=12))
we = WordsExtractor(use_lexemes=True, lowercase=True, filter_nums=True)
lemmas = [lemma for text in texts for lemma in we.extract(text)]

# Lexical dispersion
dispersion_plot(lemmas, ["партия", "рабочий", "революция", "царь"])

# Keywords relative to the frequency dictionary
freq_dict = FreqDict()
keyness_plot(
    keyness(lemmas, freq_dict, min_freq=5, top_n=10),
    keyness(lemmas, freq_dict, positive=False, min_freq=5, top_n=10),
    labels=("тексты Сталина", "частотный словарь"),
)

# Collocation network
graph = collocation_network(collocations(lemmas, window=3, min_freq=5, top_n=25))
graph.render("network", format="png")

Result:

ruts