textdescriptives 1.0.3

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

TextDescriptives

A Python library for calculating a large variety of statistics 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

• TextDescriptives has been completely re-implemented using spaCy v.3.0. The stanza implementation can be found in the stanza_version branch and will no longer be maintained.
• Check out the brand new documentation here!

👩‍💻 Usage

Import the library and add the component to your pipeline using the string name of the "textdescriptives" component factory:

import spacy
import textdescriptives as td
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("textdescriptives") 
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 to a Pandas DataFrame or a dictionary.

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

	text	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	flesch_reading_ease	flesch_kincaid_grade	smog	gunning_fog	automated_readability_index	coleman_liau_index	lix	rix	dependency_distance_mean	dependency_distance_std	prop_adjacent_dependency_relation_mean	prop_adjacent_dependency_relation_std
0	The world (...)	3.28571	3	1.54127	7	6	3.09839	1.08571	1	0.368117	35	23	0.657143	121	5	107.879	-0.0485714	5.68392	3.94286	-2.45429	-0.708571	12.7143	0.4	1.69524	0.422282	0.44381	0.0863679

Set which group(s) of metrics you want to extract using the metrics parameter (one or more of readability, dependency_distance, descriptive_stats, defaults to all)

If extract_df is called on an object created using nlp.pipe it will format the output with 1 row for each document and a column for each metric. Similarly, extract_dict will have a key for each metric and values as a list of metrics (1 per doc).

docs = nlp.pipe(['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.',
            'He felt that his whole life was some kind of dream and he sometimes wondered whose it was and whether they were enjoying it.'])

td.extract_df(docs, metrics="dependency_distance")

	text	dependency_distance_mean	dependency_distance_std	prop_adjacent_dependency_relation_mean	prop_adjacent_dependency_relation_std
0	The world (...)	1.69524	0.422282	0.44381	0.0863679
1	He felt (...)	2.56	0	0.44	0

The text column can by exluded by setting include_text to False.

Using specific components

The specific components (descriptive_stats, readability, and dependency_distance) can be loaded individually. This can be helpful if you're only interested in e.g. readability metrics or descriptive statistics and don't want to run the dependency parser.

nlp = spacy.blank("da")
nlp.add_pipe("descriptive_stats")
docs = nlp.pipe(['Da jeg var atten, tog jeg patent på ild. Det skulle senere vise sig at blive en meget indbringende forretning',
            "Spis skovsneglen, Mulle. Du vil jo gerne være med i hulen, ikk'?"])

# extract_df is clever enough to only extract metrics that are in the Doc
td.extract_df(docs, include_text = False)

	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	4.4	3	2.59615	10	10	1	1.65	1	0.852936	20	19	0.95	90	2
1	4	3.5	2.44949	6	6	3	1.58333	1	0.862007	12	12	1	53	2

Available attributes

The table below shows the metrics included in TextDescriptives and their attributes on spaCy's Doc, Span, and Token objects. For more information, see the docs.

Attribute	Component	Description
Doc._.token_length	descriptive_stats	Dict containing mean, median, and std of token length.
Doc._.sentence_length	descriptive_stats	Dict containing mean, median, and std of sentence length.
Doc._.syllables	descriptive_stats	Dict containing mean, median, and std of number of syllables per token.
Doc._.counts	descriptive_stats	Dict containing the number of tokens, number of unique tokens, proportion unique tokens, and number of characters in the Doc.
Doc._.readability	readability	Dict containing Flesch Reading Ease, Flesch-Kincaid Grade, SMOG, Gunning-Fog, Automated Readability Index, Coleman-Liau Index, LIX, and RIX readability metrics for the Doc.
Doc._.dependency_distance	dependency_distance	Dict containing the mean and standard deviation of the dependency distance and proportion adjacent dependency relations in the Doc.
Span._.token_length	descriptive_stats	Dict containing mean, median, and std of token length in the span.
Span._.counts	descriptive_stats	Dict containing the number of tokens, number of unique tokens, proportion unique tokens, and number of characters in the span.
Span._.dependency_distance	dependency_distance	Dict containing the mean dependency distance and proportion adjacent dependency relations in the Doc.
Token._.dependency_distance	dependency_distance	Dict containing the dependency distance and whether the head word is adjacent for a Token.

Authors

Developed by Lasse Hansen (@HLasse) at the Center for Humanities Computing Aarhus

Collaborators:

• Ludvig Renbo Olsen (@ludvigolsen, ludvigolsen.dk)
• Kenneth Enevoldsen (@KennethEnevoldsen)