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words / automated-readability

Licence: MIT license
Formula to detect ease of reading according to the Automated Readability Index (1967)

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automated-readability

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Formula to detect the ease of reading a text according to the automated readability index.

Install

This package is ESM only: Node 12+ is needed to use it and it must be imported instead of required.

npm:

npm install automated-readability

Use

import {automatedReadability} from 'automated-readability'

// For:
//
// The rule of rhythm in prose is not so intricate. Here,
// too, we write in groups, or phrases, as I prefer to call
// them, for the prose phrase is greatly longer and is much
// more nonchalantly uttered than the group in verse; so
// that not only is there a greater interval of continuous
// sound between the pauses, but, for that very reason,
// word is linked more readily to word by a more summary
// enunciation. Still, the phrase is the strict analogue
// of the group, and successive phrases, like successive
// groups, must differ openly in length and rhythm. The
// rule of scansion in verse is to suggest no measure but
// the one in hand; in prose, to suggest no measure at all.
// Prose must be rhythmical, and it may be as much so as
// you will; but it must not be metrical. It may be
// anything, but it must not be verse.
//
// Containing 6 sentences, 151 words, and 623 characters.
automatedReadability({
  sentence: 6,
  word: 151,
  character: 623
})
// => 10.585982…

API

This package exports the following identifiers: automatedReadability. There is no default export.

automatedReadability(counts)

Given an object containing the number of words (word), the number of sentences (sentence), and the number of characters (character) in a document, returns the grade level associated with the document.

Related

  • coleman-liau — Uses letter count instead of an error-prone syllable parser
  • dale-chall-formula — Uses a dictionary, suited for higher reading levels
  • flesch — Uses syllable count
  • flesch-kincaid — Like flesch-formula, returns U.S. grade levels
  • gunning-fog — Uses syllable count, hard to implement (needs POS-tagging and NER)
  • smog-formula — Like gunning-fog-index, without the need for advanced NLP tasks
  • spache-formula — Uses a dictionary, suited for lower reading levels

License

MIT © Titus Wormer

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