# 🧹 textcleaner-partha
[](https://pypi.org/project/textcleaner-partha/)
[](LICENSE)
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
## 🚀 Installation
### From PyPI:
```bash
pip install textcleaner-partha
```
Install directly from GitHub:
```bash
pip install git+https://github.com/partha6369/textcleaner.git
```
## 🧠 Usage
```python
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)
```
## 🔧 Parameters
The preprocess() function offers flexible control over each text cleaning step. You can selectively enable or disable operations using the parameters below:
```python
def preprocess(
text,
lowercase=True,
remove_html=True,
remove_emoji=True,
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:
```bash
pip install -r requirements.txt
```
And download the required spaCy model:
```bash
python -m spacy download en_core_web_sm
```
## 📄 License
MIT License © Dr. Partha Majumdar
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