Metadata-Version: 2.1
Name: nlp_text_cleaner
Version: 1.0.1
Summary: Clean the text for NLP project
Author-email: Sarang Mete <sarang0909.bds@gmail.com>
License: MIT License
        
        Copyright (c) 2021 Sarang Mete
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/sarang0909/nlp_text_cleaner
Keywords: nlp,text cleaning,natural-language-processing,text-cleaning,text-preprocessing
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# 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_stopwrods : A method to remove stopwords from text    
  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 &apos; ,&amp; ,&lt; 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 also and you can use them directly:    
  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 for any change. 
