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 pos_prop_DT pos_prop_NN pos_prop_VBZ pos_prop_VBN pos_prop_. pos_prop_PRP pos_prop_VBP pos_prop_IN pos_prop_RB pos_prop_VBD pos_prop_, pos_prop_WP
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 0.097561 0.121951 0.0487805 0.0487805 0.121951 0.170732 0.121951 0.121951 0.0731707 0.0243902 0.0243902 0.0243902

Set which group(s) of metrics you want to extract using the metrics parameter (one or more of readability, dependency_distance, descriptive_stats, pos_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, dependency_distance and pos_stats) 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 or part-of-speech tagger.

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._.pos_proportions pos_stats Dict of {pos_prop_POSTAG: proportion of all tokens tagged with POSTAG}. Does not create a key if no tokens in the document fit the POSTAG.
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.

GitHub

GitHub - HLasse/TextDescriptives: A Python library for calculating a large variety of statistics from text
A Python library for calculating a large variety of statistics from text - GitHub - HLasse/TextDescriptives: A Python library for calculating a large variety of statistics from text