textcleaner 0.4.9

textcleaner: text-data pre-processing library

textcleaner V0.4.9

Text-Cleaner is a utility library for text-data pre-processing. Use it before passing the text data to a model.

website - for more

Features!

• main_cleaner to do all the below in one call ! or

• remove unnecessary blank lines

• stip out a perticular character or default one

• transfer all characters to lowercase if needed

• remove numbers, symblos and stop-words from the whole text

• tokenize the text-data on one call

• stemming & lemmatization powered by NLTK

  The goal is to make basic cleaning of data hassle free. Most of the developers who are working with text data have faced this situation where data is not consumable and they end up wasting their time on these issues rather than fine tunning the model and get better accuracy. In that scenario this library can be useful and save you a tone of time.

Tech

textcleaner uses a number of open source projects to work properly:

• NLTK - for advanced cleaning

• REGEX - for regular expression

And of course textcleaner itself is open source with a public repository on GitHub.

Installation

textcleaner requires Python 3.x to run.

Install the dependencies if you have not already installed it!

• NLTK : steps to install [documentation]

• REGEX :

pip install regex

• textcleaner :

pip install textcleaner

or

pip install textcleaner==0.4.9

Usage

import textcleaner as tc
tc.main_cleaner('<FILE_NAME>')
#or
tc.document('<FILE_NAME>')

Above command will convert the text file into list of words with cleaning. Default response of the function is list of list use op argument and set it to ‘words’ and you will get a flat list of words.

Todos

• more advanced features

• ability to read more formats rather than only .txt

License

MIT

Free Software, Hell Yeah!