textstat-py 0.1.1

Pure-Python text analysis: readability, vocabulary richness, sentiment, n-grams

textstat

Text analysis for Python. Readability scores, vocabulary stats, sentiment, n-grams — no dependencies.

pip install textstat-py

Usage

from textstat import analyze, flesch_reading_ease, grade_level_consensus

text = open("essay.txt").read()

print(flesch_reading_ease(text))    # 68.4
print(grade_level_consensus(text))  # 9.2

stats = analyze(text)
# stats is a flat dict with everything:
# reading_time_min, sentiment_label, vocabulary_richness, sentence_stats, ...

CLI

textstat document.txt
cat file.txt | textstat
textstat --json report.txt

Functions

Readability

• flesch_reading_ease(text) — 0–100
• flesch_kincaid_grade(text) — US grade level
• gunning_fog(text) — years of education
• coleman_liau_index(text)
• automated_readability_index(text)
• smog_index(text)
• grade_level_consensus(text) — average across all grade metrics

Vocabulary

• lexical_diversity(text) — type-token ratio
• mattr(text, window=100) — moving-average TTR
• herdan_c(text), yule_k(text)
• hapax_legomena_ratio(text) — fraction of words appearing once
• vocabulary_richness(text) — all of the above as a dict

Counts & structure

• count_words(text), count_sentences(text), count_paragraphs(text)
• reading_time(text, wpm=200)
• sentence_stats(text), paragraph_stats(text)

Sentiment

• sentiment_polarity(text) — −1 to +1
• sentiment_label(text) — "positive" / "neutral" / "negative"

N-grams

• top_ngrams(text, n=2, k=10)
• ngram_diversity(text, n=2)
• ngram_stats(text)

Misc

• top_words(text, n=10)
• word_frequency_distribution(text)
• text_density(text)

Requirements

Python 3.8+

License

MIT