nlp-text-cleaner 1.0.8

Clean the text for NLP project

nlp_text_cleaner

About

This is a project developed to create a utility module for text cleaning/pre processing required in NLP projects

Installation

pip install nlp-text-cleaner

Usage

    import nlp_text_cleaner as cleaner
    cleaned_text = cleaner.apply_stemming("I played Cricket")

There are following methods present for text cleaning.

• split_into_sentences : A method to split text into sentences

• split_into_words : A method to split text into words

• lower_case_text : A method to convert text to lower case

• remove_punctuation : A method to remove punctuations in a text

• remove_unicode : A method to remove unicode characters in a text

• remove_leading_trailing_whitespaces : A method to remove white spaces at the begining or end of text

• remove_duplicate_whitespaces : A method to remove consecutive white spaces

• detect_language : A method to detect language of text

• correct_grammar : A method to correct spelling mistakes in a text

• remove_stopwords : A method to remove stopwords from text with optional argument to pass our own custom stopwords.

• apply_stemming : A method to apply stemming on text

• apply_lammatization : A method to apply lemmatization on text

• remove_hashtags : A method to remove hashtags in a text

• remove_hyperlinks : A method to remove hyperlinks in a text

• clean_html_code : A method to remove html entities like ' ,& ,< etc/

• replace_contraction : A method to sreplace contractions like n't,'ll etc

• get_pos_tags : A method to get POS tags of text

You can use above methods as per requirement of a use case. However,there are some default methods that you can use:

• clean_single_sentence : A default method to clean single sentence

• clean_paragraph_to_sentences : A default method to get cleaned sentences from a paragraph

• clean_paragraph : A default method to clean complete paragraph

Contributing

Please create a Pull request on 'develop' branch.

Developer Instructions

If you are using conda then go to location of environment.yml file and run:

conda env create -f environment.yml     

For pip:

pip install -r requirements.txt     

Unit Testing

1. Go inside 'tests' folder on command line.
2. Run:

    pytest -vv 

Contributors

Made with contributors-img.