An open-source NLP library: fast text cleaning and preprocessing.

Last update: Mar 18, 2022

Overview

🌴 dobbi 🦕

Takes care of all of this boring NLP stuff

Description

An open-source NLP library: fast text cleaning and preprocessing.

TL;DR

This library provides a quick and ready-to-use text preprocessing tools for text cleaning and normalization. You can simply remove hashtags, nicknames, emoji, url addresses, punctuation, whitespace and whatever.

Installation

To download dobbi, either fork this GitHub repo or simply use Pypi via pip:

$ pip install dobbi

Usage

Import the library:

import dobbi

Interaction

The library uses method chaining in order to simplify text processing:

dobbi.clean() \
    .hashtag() \
    .nickname() \
    .url() \
    .execute('Check here: https://some-url.com')

Supported methods and patterns

The process consists of three stages:

Initialization methods: initialize a dobbi Work object
Intermediate methods: chain patterns in the needed order
Terminal methods: choose if you need a function or a result

Initialization functions:

dobbi.clean()
dobbi.collect()
dobbi.replace()

Intermediate methods (pattern processing choice):

regexp() - custom regular expressions
url() - URLs
html() - HTML and "<...>" type markups
punctuation() - punctuation
hashtag() - hashtags
emoji() - emoji
emoticons() - emoticons
whitespace() - any type of whitespaces
nickname() - @-starting nicknames

Terminal methods:

execute(str) - executes chosen methods on the provided string.
function() - returns a function which is a combination of the chosen methods.

Examples

1) Clean a random Twitter message

dobbi.clean() \
    .hashtag() \
    .nickname() \
    .url() \
    .execute('#fun #lol    Why  @Alex33 is so funny? Check here: https://some-url.com')

Result:

'Why is so funny? Check here:'

2) Replace nicknames and urls with tokens

dobbi.replace() \
    .hashtag('') \
    .nickname() \
    .url('__CUSTOM_URL_TOKEN__') \
    .execute('#fun #lol    Why  @Alex33 is so funny? Check here: https://some-url.com')

Result:

'Why TOKEN_NICKNAME is so funny? Check here: __CUSTOM_URL_TOKEN__'

3) Get the text cleanup function (one-liner)

~~Please, try to avoid the in-line method chaining, as it is less readable.~~ Do as your heart tells you.

func = dobbi.clean().url().hashtag().punctuation().whitespace().html().function()
func('\t #fun #lol    Why  @Alex33 is so... funny? 
    
    \nCheck
    \there: https://some-url.com'
   )

Result:

'Why Alex33 is so funny Check here'

Chain regexp methods

dobbi.clean() \
    .regexp('#\w+') \
    .regexp('@\w+') \
    .regexp('https?://\S+') \
    .execute('#fun #lol    Why  @Alex33 is so funny? Check here: https://some-url.com')

Result:

'Why is so funny? Check here:'

Additional

Please pay attention that the functions are applied in the order you've specified them. So, you're better to chain .punctuation() as one of the last functions.

Call for collaboration 🤗

If you enjoyed the project I would be grateful if you supported it :)

Below is the list of useful features I would be happy to share with you:

Finding bugs
Making code optimizations
Writing tests
Help with new features development

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An open-source NLP library: fast text cleaning and preprocessing.

Related tags

Overview

🌴 dobbi 🦕

Description

TL;DR

Installation

Usage

Interaction

Supported methods and patterns

Examples

1) Clean a random Twitter message

2) Replace nicknames and urls with tokens

3) Get the text cleanup function (one-liner)

Additional

Call for collaboration 🤗

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Releases(v0_13)

v0_13(Oct 29, 2021)

v0_10(Oct 19, 2021)

v0_06(Oct 18, 2021)

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Owner

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