lexicalrichness 0.1.4

A small module to compute textual lexical richness (aka lexical diversity).

LexicalRichness

A small python module to compute textual lexical richness (aka lexical diversity) measures.

Lexical richness refers to the range and variety of vocabulary deployed in a text by a speaker/writer (McCarthy and Jarvis 2007). Lexical richness is used interchangeably with lexical diversity, lexical variation, lexical density, and vocabulary richness and is measured by a wide variety of indices. Uses include (but not limited to) measuring writing quality, vocabulary knowledge (Šišková 2012), speaker competence, and socioeconomic status (McCarthy and Jarvis 2007).

Installation

Install using PIP

pip install lexicalrichness

If you encounter,

ModuleNotFoundError: No module named 'textblob'

install textblob:

pip install textblob

Install manually from GitHub

git clone https://github.com/LSYS/LexicalRichness.git
cd LexicalRichness
pip install .

Run from the cloud

Try the package on the cloud (without setting anything up on your local machine) by clicking the icon here:

Quickstart

>>> from lexicalrichness import LexicalRichness

# text example
>>> text = """Measure of textual lexical diversity, computed as the mean length of sequential words in
                a text that maintains a minimum threshold TTR score.

                Iterates over words until TTR scores falls below a threshold, then increase factor
                counter by 1 and start over. McCarthy and Jarvis (2010, pg. 385) recommends a factor
                threshold in the range of [0.660, 0.750].
                (McCarthy 2005, McCarthy and Jarvis 2010)"""

# instantiate new text object (use the tokenizer=blobber argument to use the textblob tokenizer)
>>> lex = LexicalRichness(text)

# Return word count.
>>> lex.words
57

# Return (unique) word count.
>>> lex.terms
39

# Return type-token ratio (TTR) of text.
>>> lex.ttr
0.6842105263157895

# Return root type-token ratio (RTTR) of text.
>>> lex.rttr
5.165676192553671

# Return corrected type-token ratio (CTTR) of text.
>>> lex.cttr
3.6526846651686067

# Return mean segmental type-token ratio (MSTTR).
>>> lex.msttr(segment_window=25)
0.88

# Return moving average type-token ratio (MATTR).
>>> lex.mattr(window_size=25)
0.8351515151515151

# Return Measure of Textual Lexical Diversity (MTLD).
>>> lex.mtld(threshold=0.72)
46.79226361031519

# Return hypergeometric distribution diversity (HD-D) measure.
>>> lex.hdd(draws=42)
0.7468703323966486

# Return Herdan's lexical diversity measure.
>>> lex.Herdan
0.9061378160786574

# Return Summer's lexical diversity measure.
>>> lex.Summer
0.9294460323356605

# Return Dugast's lexical diversity measure.
>>> lex.Dugast
43.074336212149774

# Return Maas's lexical diversity measure.
>>> lex.Maas
0.023215679867353005

Attributes and properties

wordlist	list of words
words	number of words (w)
terms	number of unique terms (t)
preprocessor	preprocessor used
tokenizer	tokenizer used
ttr	type-token ratio computed as t / w (Chotlos 1944, Templin 1957)
rttr	root TTR computed as t / sqrt(w) (Guiraud 1954, 1960)
cttr	corrected TTR computed as t / sqrt(2w) (Carrol 1964)
Herdan	log(t) / log(w) (Herdan 1960, 1964)
Summer	log(log(t)) / log(log(w)) Summer (1966)
Dugast	(log(w) ** 2) / (log(w) - log(t) Dugast (1978)
Maas	(log(w) - log(t)) / (log(w) ** 2) Maas (1972)

Methods

msttr	Mean segmental TTR (Johnson 1944)
mattr	Moving average TTR (Covington 2007, Covington and McFall 2010)
mtld	Measure of Lexical Diversity (McCarthy 2005, McCarthy and Jarvis 2010)
hdd	HD-D (McCarthy and Jarvis 2007)

Assessing method docstrings

>>> import inspect

# docstring for hdd (HD-D)
>>> print(inspect.getdoc(LexicalRichness.hdd))

Hypergeometric distribution diversity (HD-D) score.

For each term (t) in the text, compute the probabiltiy (p) of getting at least one appearance
of t with a random draw of size n < N (text size). The contribution of t to the final HD-D
score is p * (1/n). The final HD-D score thus sums over p * (1/n) with p computed for
each term t. Described in McCarthy and Javis 2007, p.g. 465-466.
(McCarthy and Jarvis 2007)

Parameters
__________
draws: int
    Number of random draws in the hypergeometric distribution (default=42).

Returns
_______
float

Alternatively, just do

>>> print(lex.hdd.__doc__)

Hypergeometric distribution diversity (HD-D) score.

    For each term (t) in the text, compute the probabiltiy (p) of getting at least one appearance
    of t with a random draw of size n < N (text size). The contribution of t to the final HD-D
    score is p * (1/n). The final HD-D score thus sums over p * (1/n) with p computed for
    each term t. Described in McCarthy and Javis 2007, p.g. 465-466.
    (McCarthy and Jarvis 2007)

    Parameters
    ----------
    draws: int
        Number of random draws in the hypergeometric distribution (default=42).

    Returns
    -------
    float

Contributing

Author

Lucas Shen

Contributors

• Christophe Bedetti

• David Lesieur

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. See here for how to contribute to this project. See here for Contributor Code of Conduct.

License

The package is released under the MIT License.

History

0.1.2 (2018-05-09)

• First release on PyPI.

0.1.3 (2018-05-27)

• Minor fix for compatibility issue with hyphens (ascii) in python 2.

0.1.4 (2021-11-13)

• Add textblob in setup.py as requirements (to fix “ModuleNotFoundError: No module named ‘textblob’”) [Christophe Bedetti].

• Make preprocessing and tokenization optional [David Lesieur].