textcleaner-partha 0.3.1

A lightweight and reusable text preprocessing package for NLP tasks

🧹 textcleaner-partha

A lightweight and reusable text preprocessing package for NLP tasks. It cleans text by removing HTML tags and emojis, expanding contractions, correcting spelling, and performing lemmatization using spaCy.

✨ Features

•	✅ HTML tag and emoji removal
•	✅ Contraction expansion (e.g., “can’t” → “cannot”)
•	✅ Abbreviation expansion (e.g., “asap” → “as soon as possible”)
•	✅ Spelling correction with autocorrect
•	✅ Lemmatization using spaCy (en_core_web_sm)
•	✅ Filters out stopwords, punctuation, numbers
•	✅ Retains only nouns, verbs, adjectives, and adverbs
•	✅ Returns tokens in a text

🚀 Installation

From PyPI:

pip install textcleaner-partha

Install directly from GitHub:

pip install git+https://github.com/partha6369/textcleaner.git

🧠 Usage

from textcleaner_partha import preprocess

text = "I can't believe it's already raining! 😞 <p>Click here</p>"

# Default usage (all features enabled)
cleaned = preprocess(text)
print(cleaned)

# Custom usage with optional features disabled
cleaned_partial = preprocess(
    text,
    lemmatise=False,            # Skip spaCy processing (lemmatisation, POS filtering)
    correct_spelling=False,     # Skip spelling correction
    expand_contraction=False    # Skip contraction expansion
)
print(cleaned_partial)

from textcleaner_partha import get_tokens

text = "I can't believe it's already raining! 😞 <p>Click here</p>"

# Default usage (all features enabled)
tokens = get_tokens(text)
print(tokens)

# Custom usage with optional features disabled
tokens_partial = get_tokens(
    text,
    lemmatise=False,            # Skip spaCy processing (lemmatisation, POS filtering)
    correct_spelling=False,     # Skip spelling correction
    expand_contraction=False    # Skip contraction expansion
)
print(tokens_partial)

🔧 Parameters

The preprocess() and get_tokens() functions offer flexible control over each text cleaning step. You can selectively enable or disable operations using the parameters below:

def preprocess(
    text,
    lowercase=True,
    remove_html=True,
    remove_emoji=True,
    remove_whitespace=True,
    remove_punct=False,
    expand_contraction=True,
    expand_abbrev=True,
    correct_spelling=True,
    lemmatise=True,
)

def get_tokens(
    text,
    lowercase=True,
    remove_html=True,
    remove_emoji=True,
    remove_whitespace=True,
    remove_punct=False,
    expand_contraction=True,
    expand_abbrev=True,
    correct_spelling=True,
    lemmatise=True,
)

📦 Dependencies

•	spacy
•	autocorrect
•	contractions

You can install them manually or via the included requirements.txt:

pip install -r requirements.txt

And download the required spaCy model:

python -m spacy download en_core_web_sm

📄 License

MIT License © Dr. Partha Majumdar