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Project Description

Preprocessor

Preprocessor is a preprocessing library for tweet data written in Python.

When building Machine Learning systems based on tweet data, a preprocessing is required. This library makes it easy to clean, parse or tokenize the tweets.

Features

Currently supports cleaning, tokenizing and parsing:

  • URLs
  • Hashtags
  • Mentions
  • Reserved words (RT, FAV)
  • Emojis
  • Smileys

Supports Python 2.7 and 3.3+

Usage

Basic cleaning:

>>> import preprocessor as p
>>> p.clean('Preprocessor is #awesome πŸ‘ https://github.com/s/preprocessor')
'Preprocessor is'

Tokenizing:

>>> p.tokenize('Preprocessor is #awesome πŸ‘ https://github.com/s/preprocessor')
'Preprocessor is $HASHTAG$ $EMOJI$ $URL$'

Parsing:

>>> parsed_tweet = p.parse('Preprocessor is #awesome https://github.com/s/preprocessor')
<preprocessor.parse.ParseResult instance at 0x10f430758>
>>> parsed_tweet.urls
[(25:58) => https://github.com/s/preprocessor]
>>> parsed_tweet.urls[0].start_index
25
>>> parsed_tweet.urls[0].match
'https://github.com/s/preprocessor'
>>> parsed_tweet.urls[0].end_index
58

Fully customizable:

>>> p.set_options(p.OPT.URL, p.OPT.EMOJI)
>>> p.clean('Preprocessor is #awesome πŸ‘ https://github.com/s/preprocessor')
'Preprocessor is #awesome'

Preprocessor will go through all of the options by default unless you specify some options.

Available Options:

Option Name Option Short Code
URL p.OPT.URL
Mention p.OPT.MENTION
Hashtag p.OPT.HASHTAG
Reserved Words p.OPT.RESERVED
Emoji p.OPT.EMOJI
Smiley p.OPT.SMILEY
Number p.OPT.NUMBER

Installation

using pip:

$ pip install tweet-preprocessor
Release History

Release History

0.5.0

This version

History Node

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Changelog content for this version goes here.

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0.4.0

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0.3.0

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0.2.0

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0.1.2

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
tweet-preprocessor-0.5.0.tar.gz (6.3 kB) Copy SHA256 Checksum SHA256 – Source Feb 2, 2016

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