Text Preprocessing Toolkit (TPT)

Version: 0.0.1
Author: Gaurav Jaiswal
A comprehensive Python toolkit for preprocessing text, designed to simplify NLP workflows. This package provides various utilities like stopword removal, punctuation handling, spell-checking, lemmatization, and more to clean and preprocess text effectively.

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Features

• Remove Punctuation: Strips punctuation marks from text.
• Remove Stopwords: Removes common stopwords to reduce noise in textual data.
• Remove Special Characters: Cleans text by removing unnecessary symbols.
• Lowercase Conversion: Standardizes text to lowercase.
• Spell Correction: Identifies and corrects misspelled words.
• Lemmatization: Converts words to their base forms.
• Stemming: Reduces words to their root forms using a stemming algorithm.
• HTML Tag Removal: Cleans HTML tags from the text.
• URL Removal: Detects and removes URLs.
• Customizable Pipeline: Allows users to apply preprocessing steps in a specified order.
• Quick Dataset Preview: Provides a summary of text datasets, including word and character counts.

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Installation

Clone the repository or install the package using pip:

pip install Text_Preprocessing_Toolkit

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Usage

Import the Package

from TPT import TPT

Initialize the Toolkit

You can add custom stopwords during initialization:

tpt = TPT(custom_stopwords=["example", "custom"])

Preprocess Text with Default Pipeline

text = "This is an <b>example</b> sentence with a URL: https://example.com."
processed_text = tpt.preprocess(text)
print(processed_text)

Customize Preprocessing Steps

custom_steps = ["lowercase", "remove_punctuation", "remove_stopwords"]
processed_text = tpt.preprocess(text, steps=custom_steps)
print(processed_text)

Quick Dataset Summary

texts = [
    "This is a sample text.",
    "Another <b>example</b> with HTML tags and a URL: https://example.com.",
    "Spellngg errors corrected!",
]
tpt.head(texts, n=3)

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Available Methods

Method	Description
remove_punctuation	Removes punctuation from text.
remove_stopwords	Removes stopwords from text.
remove_special_characters	Cleans text by removing special characters.
remove_url	Removes URLs from the text.
remove_html_tags	Strips HTML tags from text.
correct_spellings	Corrects spelling mistakes in the text.
lowercase	Converts text to lowercase.
lemmatize_text	Lemmatizes text using WordNet.
stem_text	Applies stemming to reduce words to their root forms.
preprocess	Applies a series of preprocessing steps to the input text.
head	Displays a quick summary of a text dataset.

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Example Output

Input

This is a <b>sample</b> text with a URL: https://example.com. Check spellngg errors!

Output (Default Pipeline)

sample text check spelling errors

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Requirements

• Python >= 3.8
• Libraries: nltk, pandas, spellchecker, IPython

To install the dependencies:

pip install -r requirements.txt

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Contributing

Contributions are welcome! To contribute:

1. Fork this repository.
2. Clone your forked repository.
3. Create a new branch for your feature.
4. Make your changes, write tests, and ensure the code passes.
5. Submit a pull request for review.

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Testing

To test the package locally:

1. Install development dependencies:

   pip install pytest
   

2. Run tests:

   pytest
   

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Author

• Gaurav Jaiswal
  GitHub