textdescriptives 2.4.4

A library for calculating a variety of features from text using spaCy

TextDescriptives

A Python library for calculating a large variety of metrics from text(s) using spaCy v.3 pipeline components and extensions. TextDescriptives can be used to calculate several descriptive statistics, readability metrics, and metrics related to dependency distance.

🔧 Installation

pip install textdescriptives

📰 News

• Version 2.0 out with a new API, a new component, updated documentation, and tutorials! Components are now called by "textdescriptives/{metric_name}. New coherence component for calculating the semantic coherence between sentences. See the documentation for tutorials and more information!

⚡ Quick Start

Use extract_metrics to quickly extract your desired metrics. To see available methods you can simply run:

import textdescriptives as td
td.get_valid_metrics()
# {'quality', 'readability', 'all', 'descriptive_stats', 'dependency_distance', 'pos_proportions', 'information_theory', 'coherence'}

Set the spacy_model parameter to specify which spaCy model to use, otherwise, TextDescriptives will auto-download an appropriate one based on lang. If lang is set, spacy_model is not necessary and vice versa.

Specify which metrics to extract in the metrics argument. None extracts all metrics.

import textdescriptives as td

text = "The world is changed. I feel it in the water. I feel it in the earth. I smell it in the air. Much that once was is lost, for none now live who remember it."
# will automatically download the relevant model (´en_core_web_lg´) and extract all metrics
df = td.extract_metrics(text=text, lang="en", metrics=None)

# specify spaCy model and which metrics to extract
df = td.extract_metrics(text=text, spacy_model="en_core_web_lg", metrics=["readability", "coherence"])

Usage with spaCy

To integrate with other spaCy pipelines, import the library and add the component(s) to your pipeline using the standard spaCy syntax. Available components are descriptive_stats, readability, dependency_distance, pos_proportions, coherence, and quality prefixed with textdescriptives/.

If you want to add all components you can use the shorthand textdescriptives/all.

import spacy
import textdescriptives as td
# load your favourite spacy model (remember to install it first using e.g. `python -m spacy download en_core_web_sm`)
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("textdescriptives/all") 
doc = nlp("The world is changed. I feel it in the water. I feel it in the earth. I smell it in the air. Much that once was is lost, for none now live who remember it.")

# access some of the values
doc._.readability
doc._.token_length

TextDescriptives includes convenience functions for extracting metrics from a Doc to a Pandas DataFrame or a dictionary.

td.extract_dict(doc)
td.extract_df(doc)

	text	first_order_coherence	second_order_coherence	pos_prop_DET	pos_prop_NOUN	pos_prop_AUX	pos_prop_VERB	pos_prop_PUNCT	pos_prop_PRON	pos_prop_ADP	pos_prop_ADV	pos_prop_SCONJ	flesch_reading_ease	flesch_kincaid_grade	smog	gunning_fog	automated_readability_index	coleman_liau_index	lix	rix	n_stop_words	alpha_ratio	mean_word_length	doc_length	proportion_ellipsis	proportion_bullet_points	duplicate_line_chr_fraction	duplicate_paragraph_chr_fraction	duplicate_5-gram_chr_fraction	duplicate_6-gram_chr_fraction	duplicate_7-gram_chr_fraction	duplicate_8-gram_chr_fraction	duplicate_9-gram_chr_fraction	duplicate_10-gram_chr_fraction	top_2-gram_chr_fraction	top_3-gram_chr_fraction	top_4-gram_chr_fraction	symbol_#_to_word_ratio	contains_lorem ipsum	passed_quality_check	dependency_distance_mean	dependency_distance_std	prop_adjacent_dependency_relation_mean	prop_adjacent_dependency_relation_std	token_length_mean	token_length_median	token_length_std	sentence_length_mean	sentence_length_median	sentence_length_std	syllables_per_token_mean	syllables_per_token_median	syllables_per_token_std	n_tokens	n_unique_tokens	proportion_unique_tokens	n_characters	n_sentences
0	The world is changed(...)	0.633002	0.573323	0.097561	0.121951	0.0731707	0.170732	0.146341	0.195122	0.0731707	0.0731707	0.0487805	107.879	-0.0485714	5.68392	3.94286	-2.45429	-0.708571	12.7143	0.4	24	0.853659	2.95122	41	0	0	0	0	0.232258	0.232258	0	0	0	0	0.0580645	0.174194	0	0	False	False	1.77524	0.553188	0.457143	0.0722806	3.28571	3	1.54127	7	6	3.09839	1.08571	1	0.368117	35	23	0.657143	121	5

📖 Documentation

TextDescriptives has a detailed documentation as well as a series of Jupyter notebook tutorials. All the tutorials are located in the docs/tutorials folder and can also be found on the documentation website.

Documentation
📚 Getting started	Guides and instructions on how to use TextDescriptives and its features.
😎 Tutorials	Detailed tutorials on how to make the most of TextDescriptives
📰 News and changelog	New additions, changes and version history.
🎛 API References	The detailed reference for TextDescriptive's API. Including function documentation
📄 Paper	The preprint of the TextDescriptives paper.