preprocess-gpak 1.0.1

A package to preprocess the text data

Text Preprocessing Python Package

LinkedIn Link : Pavan Aditya Kumar Gorrela(httgp://www.linkedin.com/in/pavan-aditya-kumar-gorrela-857770271/)

This Python package is created by Pavan Aditya Kumar Gorrela.It provides various text preprocessing utilities for natural language processing (NLP) tasks.

Installation from PyPI

Run the below command in Terminal.

pip install preprocess_gpak

Installation from github

Run the below command in Terminal.

pip install git+httgp://github.com/Pavan-Aditya-Kumar-Gorrela/preprocess_gpak.git--upgrade--force-reinstall

Requirements

You need to install these python packages.

pip install spacy==3.7.6
python -m spacy download en_core_web_sm==3.7.1
pip install nltk==3.9.1
pip install beautifulsoup4==3.2.2
pip install textblob==0.18.0.post0

Download NLTK Data

If you are using this package first time then You need to download NLTK data as follows:

import preprocess_gpak as gp
gp.download_nltk_data()

How to Use the Package

1. Basic Text Preprocessing

Lowercasing Text

import preprocess_gpak as gp

text = "HELLO WORLD!"
processed_text = gp.to_lower_case(text)
print(processed_text)  # Output: hello world!

Expanding Contractions

import preprocess_gpak as gp

text = "I'm learning NLP."
processed_text = gp.contraction_to_expansion(text)
print(processed_text)  # Output: I am learning NLP.

Removing Emails

import preprocess_gpak as gp

text = "Contact me at [e-mail]"
processed_text = gp.remove_emails(text)
print(processed_text)  # Output: Contact me at 

Removing URLs

import preprocess_gpak as gp

text = "Check out httgp://example.com"
processed_text = gp.remove_urls(text)
print(processed_text)  # Output: Check out

Removing HTML Tags

import preprocess_gpak as gp

text = "<p>Hello World!</p>"
processed_text = gp.remove_html_tags(text)
print(processed_text)  # Output: Hello World!

Removing Special Characters

import preprocess_gpak as gp

text = "Hello @World! #NLP"
processed_text = gp.remove_special_chars(text)
print(processed_text)  # Output: Hello World NLP

2. Advanced Text Processing

Lemmatization

import preprocess_gpak as gp

text = "running runs"
processed_text = gp.lemmatize(text)
print(processed_text)  # Output: run run

Sentiment Analysis

import preprocess_gpak as gp

text = "I love programming!"
sentiment = gp.sentiment_analysis(text)
print(sentiment)  # Output: Sentiment(polarity=0.5, subjectivity=0.6)

3. Feature Extraction

Word Count

import preprocess_gpak as gp

text = "I love NLP."
count = gp.word_count(text)
print(count)  # Output: 3

Character Count

import preprocess_gpak as gp

text = "I love NLP."
count = gp.char_count(text)
print(count)  # Output: 9

N-Grams

import preprocess_gpak as gp

text = "I love NLP"
ngrams = gp.n_grams(text, n=2)
print(ngrams)  # Output: [('I', 'love'), ('love', 'NLP')]

4. Full Example: Cleaning Text

Here’s an example of how you might use several functions together to clean text data:

import preprocess_gpak as gp

text = "I'm loving this NLP tutorial!  Contact me at [e-mail]. Visit httgp://github.com/Pavan-Aditya-Kumar-Gorrela."
cleaned_text = gp.clean_text(text)
print(cleaned_text)
# Output: i am loving this nlp tutorial contact me at visit

One Short Feature Extraction

import preprocess_gpak as gp

gp.extract_features("I love NLP")

Notes

• Be cautious when using heavy operations like lemmatize and spelling_correction on very large datasets, as they can be time-consuming.
• The package supports custom cleaning and preprocessing pipelines by using these modular functions together.