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https://github.com/abitdodgy/words_counted

A Ruby natural language processor.
https://github.com/abitdodgy/words_counted

natural-language-processing nlp ruby rubynlp word-counter wordcount wordscounter

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A Ruby natural language processor.

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# WordsCounted



> We are all in the gutter, but some of us are looking at the stars.

>

> -- Oscar Wilde



WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.



**Are you using WordsCounted to do something interesting?** Please [tell me about it][8].





  Gem Version




[RubyDoc documentation][7].



### Demo



Visit [this website][4] for one example of what you can do with WordsCounted.



### Features



* Out of the box, get the following data from any string or readable file, or URL:

    * Token count and unique token count

    * Token densities, frequencies, and lengths

    * Char count and average chars per token

    * The longest tokens and their lengths

    * The most frequent tokens and their frequencies.

* A flexible way to exclude tokens from the tokeniser. You can pass a **string**, **regexp**, **symbol**, **lambda**, or an **array** of any combination of those types for powerful tokenisation strategies.

* Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): *Bayrūt* is treated as `["Bayrūt"]` and not `["Bayr", "ū", "t"]`, for example.

* Opens and reads files. Pass in a file path or a url instead of a string.



## Installation



Add this line to your application's Gemfile:



    gem 'words_counted'



And then execute:



    $ bundle



Or install it yourself as:



    $ gem install words_counted



## Usage



Pass in a string or a file path, and an optional filter and/or regexp.



```ruby

counter = WordsCounted.count(

  "We are all in the gutter, but some of us are looking at the stars."

)



# Using a file

counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

```



`.count` and `.from_file` are convenience methods that take an input, tokenise it, and return an instance of `WordsCounted::Counter` initialized with the tokens. The `WordsCounted::Tokeniser` and `WordsCounted::Counter` classes can be used alone, however.



## API



### WordsCounted



**`WordsCounted.count(input, options = {})`**



Tokenises input and initializes a `WordsCounted::Counter` object with the resulting tokens.



```ruby

counter = WordsCounted.count("Hello Beirut!")

````



Accepts two options: `exclude` and `regexp`. See [Excluding tokens from the analyser][5] and [Passing in a custom regexp][6] respectively.



**`WordsCounted.from_file(path, options = {})`**



Reads and tokenises a file, and initializes a `WordsCounted::Counter` object with the resulting tokens.



```ruby

counter = WordsCounted.from_file("hello_beirut.txt")

````



Accepts the same options as `.count`.



### Tokeniser



The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.



Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.



**`#tokenise([pattern: TOKEN_REGEXP, exclude: nil])`**



```ruby

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise



# With `exclude`

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")



# With `pattern`

tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

```



See [Excluding tokens from the analyser][5] and [Passing in a custom regexp][6] for more information.



### Counter



The `WordsCounted::Counter` class allows you to collect various statistics from an array of tokens.



**`#token_count`**



Returns the token count of a given string.



```ruby

counter.token_count #=> 15

```



**`#token_frequency`**



Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.



```ruby

counter.token_frequency



[

  ["the", 2],

  ["are", 2],

  ["we",  1],

  # ...

  ["all", 1]

]

```



**`#most_frequent_tokens`**



Returns a hash where each key-value pair is a token and its frequency.



```ruby

counter.most_frequent_tokens



{ "are" => 2, "the" => 2 }

```



**`#token_lengths`**



Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.



```ruby

counter.token_lengths



[

  ["looking", 7],

  ["gutter",  6],

  ["stars",   5],

  # ...

  ["in",      2]

]

```



**`#longest_tokens`**



Returns a hash where each key-value pair is a token and its length.



```ruby

counter.longest_tokens



{ "looking" => 7 }

```



**`#token_density([ precision: 2 ])`**



Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a `precision` argument, which must be a float.



```ruby

counter.token_density



[

  ["are",     0.13],

  ["the",     0.13],

  ["but",     0.07 ],

  # ...

  ["we",      0.07 ]

]

```



**`#char_count`**



Returns the char count of tokens.



```ruby

counter.char_count #=> 76

```



**`#average_chars_per_token([ precision: 2 ])`**



Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.



```ruby

counter.average_chars_per_token #=> 4

```



**`#uniq_token_count`**



Returns the number of unique tokens.



```ruby

counter.uniq_token_count #=> 13

```



## Excluding tokens from the tokeniser



You can exclude anything you want from the input by passing the `exclude` option. The exclude option accepts a variety of filters and is extremely flexible.



1. A *space-delimited* string. The filter will normalise the string.

2. A regular expression.

3. A lambda.

4. A symbol that names a predicate method.  For example `:odd?`.

5. An array of any combination of the above.



```ruby

tokeniser =

  WordsCounted::Tokeniser.new(

    "Magnificent! That was magnificent, Trevor."

  )



# Using a string

tokeniser.tokenise(exclude: "was magnificent")

# => ["that", "trevor"]



# Using a regular expression

tokeniser.tokenise(exclude: /trevor/)

# => ["magnificent", "that", "was", "magnificent"]



# Using a lambda

tokeniser.tokenise(exclude: ->(t) { t.length < 4 })

# => ["magnificent", "that", "magnificent", "trevor"]



# Using symbol

tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")

tokeniser.tokenise(exclude: :ascii_only?)

# => ["محمد"]



# Using an array

tokeniser = WordsCounted::Tokeniser.new(

  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"

)

tokeniser.tokenise(

  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]

)

# => ["هي", "سامي", "وداني"]

```



## Passing in a custom regexp



The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means *twenty-one* is treated as one token. So is *Mohamad's*.



```ruby

/[\p{Alpha}\-']+/

```



You can pass your own criteria as a Ruby regular expression to split your string as desired.



For example, if you wanted to include numbers, you can override the regular expression:



```ruby

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)

counter.tokens

#=> ["numbers", "1", "2", "and", "3"]

```



## Opening and reading files



Use the `from_file` method to open files. `from_file` accepts the same options as `.count`. The file path can be a URL.



```ruby

counter = WordsCounted.from_file("url/or/path/to/file.text")

```



## Gotchas



A hyphen used in leu of an *em* or *en* dash will form part of the token. This affects the tokeniser algorithm.



```ruby

counter = WordsCounted.count("How do you do?-you are well, I see.")

counter.token_frequency



[

  ["do",   2],

  ["how",  1],

  ["you",  1],

  ["-you", 1], # WTF, mate!

  ["are",  1],

  # ...

]

```



In this example `-you` and `you` are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.



### A note on case sensitivity



The program will normalise (downcase) all incoming strings for consistency and filters.



## Roadmap



### Ability to open URLs



```ruby

def self.from_url

  # open url and send string here after removing html

end

```



## Contributors



See [contributors][3].



## Contributing



1. Fork it

2. Create your feature branch (`git checkout -b my-new-feature`)

3. Commit your changes (`git commit -am 'Add some feature'`)

4. Push to the branch (`git push origin my-new-feature`)

5. Create new Pull Request



  [2]: http://www.rubydoc.info/gems/words_counted

  [3]: https://github.com/abitdodgy/words_counted/graphs/contributors

  [4]: http://rubywordcount.com

  [5]: https://github.com/abitdodgy/words_counted#excluding-tokens-from-the-analyser

  [6]: https://github.com/abitdodgy/words_counted#passing-in-a-custom-regexp

  [7]: http://www.rubydoc.info/gems/words_counted/

  [8]: https://github.com/abitdodgy/words_counted/issues/new