Calculate statistical features from text
Project description
textstat
Python package to calculate statistics from text to determine readability, complexity and grade level of a particular corpus.
Usage
>>> import textstat
>>> test_data = (
"Playing games has always been thought to be important to "
"the development of well-balanced and creative children; "
"however, what part, if any, they should play in the lives "
"of adults has never been researched that deeply. I believe "
"that playing games is every bit as important for adults "
"as for children. Not only is taking time out to play games "
"with our children and other adults valuable to building "
"interpersonal relationships but is also a wonderful way "
"to release built up tension."
)
>>> textstat.flesch_reading_ease(test_data)
>>> textstat.smog_index(test_data)
>>> textstat.flesch_kincaid_grade(test_data)
>>> textstat.coleman_liau_index(test_data)
>>> textstat.automated_readability_index(test_data)
>>> textstat.dale_chall_readability_score(test_data)
>>> textstat.difficult_words(test_data)
>>> textstat.linsear_write_formula(test_data)
>>> textstat.gunning_fog(test_data)
>>> textstat.text_standard(test_data)
The argument (text) for all the defined functions remains the same - i.e the text for which statistics need to be calculated.
Install
You can install textstat either via the Python Package Index (PyPI) or from source.
Install using pip
pip install textstat
Install using easy_install
easy_install textstat
Install lastest version from GitHub
git clone https://github.com/shivam5992/textstat.git
cd textstat
pip install .
Install from PyPI
Download the latest version of textstat from http://pypi.python.org/pypi/textstat/
You can install it by doing the following:
tar xfz textstat-*.tar.gz
cd textstat-*/
python setup.py build
python setup.py install # as root
List of Functions
Syllable Count
textstat.syllable_count(text, lang='en_US')
Returns the number of syllables present in the given text.
Uses the Python module Pyphen
for syllable calculation. Optional lang specifies to
Pyphen which language dictionary to use.
Default is 'en_US', 'en_GB' will also work.
Lexicon Count
textstat.lexicon_count(text, removepunct=True)
Calculates the number of words present in the text.
Optional removepunct specifies whether we need to take
punctuation symbols into account while counting lexicons.
Default value is True, which removes the punctuation
before counting lexicon items.
Sentence Count
textstat.sentence_count(text)
Returns the number of sentences present in the given text.
The Flesch Reading Ease formula
textstat.flesch_reading_ease(text)
Returns the Flesch Reading Ease Score.
The following table can be helpful to assess the ease of readability in a document.
The table is an example of values. While the maximum score is 121.22, there is no limit on how low the score can be. A negative score is valid.
| Score | Difficulty |
|---|---|
| 90-100 | Very Easy |
| 80-89 | Easy |
| 70-79 | Fairly Easy |
| 60-69 | Standard |
| 50-59 | Fairly Difficult |
| 30-49 | Difficult |
| 0-29 | Very Confusing |
Further reading on Wikipedia
The Flesch-Kincaid Grade Level
textstat.flesch_kincaid_grade(text)
Returns the Flesch-Kincaid Grade of the given text. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
The Fog Scale (Gunning FOG Formula)
textstat.gunning_fog(text)
Returns the FOG index of the given text. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
The SMOG Index
textstat.smog_index(text)
Returns the SMOG index of the given text. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Texts of fewer than 30 sentences are statistically invalid, because the SMOG formula was normed on 30-sentence samples. textstat requires atleast 3 sentences for a result.
Further reading on Wikipedia
Automated Readability Index
textstat.automated_readability_index(text)
Returns the ARI (Automated Readability Index) which outputs a number that approximates the grade level needed to comprehend the text.
For example if the ARI is 6.5, then the grade level to comprehend the text is 6th to 7th grade.
Further reading on Wikipedia
The Coleman-Liau Index
textstat.coleman_liau_index(text)
Returns the grade level of the text using the Coleman-Liau Formula. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
Linsear Write Formula
textstat.linsear_write_formula(text)
Returns the grade level using the Linsear Write Formula. This is a grade formula in that a score of 9.3 means that a ninth grader would be able to read the document.
Further reading on Wikipedia
Dale-Chall Readability Score
textstat.dale_chall_readability_score(text)
Different from other tests, since it uses a lookup table of the most commonly used 3000 English words. Thus it returns the grade level using the New Dale-Chall Formula.
| Score | Understood by |
|---|---|
| 4.9 or lower | average 4th-grade student or lower |
| 5.0–5.9 | average 5th or 6th-grade student |
| 6.0–6.9 | average 7th or 8th-grade student |
| 7.0–7.9 | average 9th or 10th-grade student |
| 8.0–8.9 | average 11th or 12th-grade student |
| 9.0–9.9 | average 13th to 15th-grade (college) student |
Further reading on Wikipedia
Readability Consensus based upon all the above tests
textstat.text_standard(text, float_output=False)
Based upon all the above tests, returns the estimated school grade level required to understand the text.
Optional float_output allows the score to be returned as a
float. Defaults to False.
Contributing
If you find any problems, you should open an issue.
If you can fix an issue you've found, or another issue, you should open a pull request.
- Fork this repository on GitHub to start making your changes to the master branch (or branch off of it).
- Write a test which shows that the bug was fixed or that the feature works as expected.
- Send a pull request!
Development setup
It is recommended you use a virtual environment, or Pipenv to keep your development work isolated from your systems Python installation.
$ git clone https://github.com/<yourname>/textstat.git # Clone the repo from your fork
$ cd textstat
$ pip install -r requirements.txt # Install all dependencies
$ # Make changes
$ python -m unittest test.py # Run tests
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