Text Metrics is a Ruby library for analysing text. It was inspired by the Python Textstat library and the Ruby port of Textstat.

It gives you the everyday counts you need — words, characters, sentences and syllables — plus readability scores such as Flesch Reading Ease, Flesch-Kincaid Grade Level, SMOG, Gunning Fog, Coleman-Liau Index and LIX. English and French are supported.

It is battle-tested in production on millions of student writings at Plume.

Readability scores such as Flesch, Flesch-Kincaid and SMOG depend heavily on syllable counts. Many libraries estimate those counts with hyphenation rules, which are fast but often wrong. Text Metrics starts from real pronunciation data instead:

• English uses the CMU Pronouncing Dictionary, which provides pronunciation-derived syllable counts for about 126,000 words. Hyphenation is only used when a word is not in the dictionary. Against CMUdict as ground truth, a hyphenation-only approach gets the syllable count wrong for about 46% of dictionary words.
• French uses counts derived from the Lexique database, plus a vowel heuristic and an exceptions list for the words the heuristic misses.

That makes syllable-sensitive scores more reliable, especially for longer or less common words.

• Scores are computed from full-precision ratios and returned unclamped. A Flesch Reading Ease score can legitimately be above 100 for very easy text, or below 0 for very difficult text.
• French Flesch Reading Ease uses the Kandel-Moles (1958) adaptation: 207 − 1.015 × (words/sentences) − 73.6 × (syllables/words). Because the formula starts from 207, very easy French text can score slightly above 100.
• Flesch-Kincaid Grade maps to a US school grade. It uses the same formula for every language because there is no validated French adaptation.
• Gunning Fog uses words with three or more syllables as complex words, matching the same syllable counts used by SMOG.
• Coleman-Liau counts alphabetic letters only (not digits or punctuation), per its definition.
• Automated Readability Index counts characters (letters, digits and punctuation, spaces excluded), per its original "strokes" definition — so it uses characters_count, unlike Coleman-Liau which uses letters only.
• Type-token ratio is distinct-words over total-words and is sensitive to text length: longer texts trend lower because words repeat. Compare it only across texts of similar length.

Basic metrics:

• word count
• character count
• sentence count
• polysyllabic word count (three or more syllables)
• long word count (more than six characters)
• average syllables per word
• average letters per word
• average words per sentence
• average characters per sentence
• type-token ratio (lexical diversity)
• reading time (minutes, configurable words-per-minute)

Readability tests:

• Flesch Reading Ease
• Flesch-Kincaid Grade Level
• Smog Index
• Gunning Fog Index
• Coleman-Liau Index
• Lix Index
• Automated Readability Index

Text Metrics is not published to RubyGems yet. For now, install it from GitHub:

# Gemfile
gem "text-metrics", "~> 1.0.0"

• Ruby (MRI) >= 3.1.0

metrics = TextMetrics.new("This gem analyses all kinds of text.")

# Get every metric at once:
metrics.to_h
# {
#   words_count: 7, characters_count: 30, sentences_count: 1, syllables_count: 10,
#   punctuation_count: 1, polysyllabic_words_count: 1, long_words_count: 1,
#   syllables_per_word_average: 1.4, letters_per_word_average: 4.14,
#   words_per_sentence_average: 7.0, characters_per_sentence_average: 30.0,
#   words_per_punctuation_average: 7.0, punctuation_per_sentence_average: 1.0,
#   type_token_ratio: 1.0, flesch_reading_ease: 78.87, flesch_kincaid_grade: 4.0,
#   lix: 21.29, smog_index: 0.0, gunning_fog_index: 8.5, coleman_liau_index: 4.33,
#   automated_readability_index: 2.3, reading_time: 0.04
# }

# Or ask for a single metric:
metrics.words_count                     # => 7
metrics.characters_count                # => 30
metrics.flesch_reading_ease             # => 78.87
metrics.flesch_kincaid_grade            # => 4.0
metrics.gunning_fog_index                # => 8.5

# Reading time is in minutes; pass a custom reading pace if you like:
metrics.reading_time                    # => 0.04   (at the default 200 wpm)
metrics.reading_time(wpm: 130)          # => 0.05

American English (:en_us) is the default. :en is accepted as an alias for :en_us. To analyse French text, pass language: :fr:

TextMetrics.new("Bonjour le monde.", language: :fr)
TextMetrics.new("Hello.", language: :en)    # same as :en_us

Unsupported languages raise TextMetrics::Error.

Global defaults can be set via TextMetrics.configure. Call this once at boot time — in a Rails initializer, for example:

TextMetrics.configure do |config|
  # Words per minute used by #reading_time (default: 200).
  config.wpm = 250
end

In a Rails app, generate the initializer with:

rails generate text_metrics:install

This creates config/initializers/text_metrics.rb with the options commented out at their defaults.

Per-call overrides are still supported and always take precedence over the global config:

metrics.reading_time(wpm: 130)

Levenshtein distance compares two strings, so it is exposed on the TextMetrics module:

TextMetrics.distance("kitten", "sitting")   # => 3      raw edit distance
TextMetrics.similarity("kitten", "sitting") # => 57.14  0–100 score (100.0 == identical)

Bug reports and pull requests are welcome on GitHub.

This gem was inspired by Textstat in Python and the Ruby Textstat port. It was generated from the newgem template by @palkan.

English syllable counts come from the CMU Pronouncing Dictionary (unrestricted use), with text-hyphen as a fallback. French syllable counts are derived from Lexique.

The gem is available as open source under the terms of the MIT License.