kleantext 0.1.0

A Python module for preprocessing text for NLP tasks

kleantext

A Python package for preprocessing textual data for machine learning and natural language processing tasks. It includes functionality for:

• Converting text to lowercase (optional case-sensitive mode)
• Removing HTML tags, punctuation, numbers, and special characters
• Handling emojis (removal or conversion to textual descriptions)
• Handling negations
• Removing or retaining specific patterns (hashtags, mentions, etc.)
• Removing stopwords (with customizable stopword lists)
• Stemming and lemmatization
• Correcting spelling (optional)
• Expanding contractions and slangs
• Named Entity Recognition (NER) masking (e.g., replacing entities with placeholders)
• Detecting and translating text to a target language
• Profanity filtering
• Customizable text preprocessing pipeline

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Installation

Option 1: Clone or Download

1. Clone the repository using:

   git clone https://github.com/your-username/kleantext.git
   

2. Navigate to the project directory:

   cd kleantext
   

Option 2: Install via pip (if published)

pip install kleantext

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Usage

Quick Start

from kleantext.preprocessor import TextPreprocessor

# Initialize the preprocessor with custom settings
preprocessor = TextPreprocessor(
    remove_stopwords=True,
    perform_spellcheck=True,
    use_stemming=False,
    use_lemmatization=True,
    custom_stopwords={"example", "test"},
    case_sensitive=False,
    detect_language=True,
    target_language="en"
)

# Input text
text = "This is an example! Isn't it great? Visit https://example.com for more 😊."

# Preprocess the text
clean_text = preprocessor.clean_text(text)
print(clean_text)  # Output: "this is isnt it great visit for more"

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Features and Configuration

1. Case Sensitivity

Control whether the text should be converted to lowercase:

preprocessor = TextPreprocessor(case_sensitive=True)

2. Removing HTML Tags

Automatically remove HTML tags like <div> or <p>.

3. Emoji Handling

Convert emojis to text or remove them entirely:

import emoji
text = emoji.demojize("😊 Hello!")  # Output: ":blush: Hello!"

4. Stopword Removal

Remove common stopwords, with support for custom lists:

custom_stopwords = {"is", "an", "the"}
preprocessor = TextPreprocessor(custom_stopwords=custom_stopwords)

5. Slang and Contraction Expansion

Expand contractions like "can't" to "cannot":

text = "I can't go"
expanded_text = preprocessor.clean_text(text)

6. Named Entity Recognition (NER) Masking

Mask entities like names, organizations, or dates using spacy:

text = "Barack Obama was the 44th President of the USA."
masked_text = preprocessor.clean_text(text)

7. Profanity Filtering

Censor offensive words:

text = "This is a badword!"
filtered_text = preprocessor.clean_text(text)

8. Language Detection and Translation

Detect the text's language and translate it:

preprocessor = TextPreprocessor(detect_language=True, target_language="en")
text = "Bonjour tout le monde"
translated_text = preprocessor.clean_text(text)  # Output: "Hello everyone"

9. Tokenization

Tokenize text for further NLP tasks:

from nltk.tokenize import word_tokenize
tokens = word_tokenize("This is an example.")
print(tokens)  # Output: ['This', 'is', 'an', 'example', '.']

---

Advanced Configuration

Create a custom pipeline by enabling or disabling specific cleaning steps:

pipeline = ["lowercase", "remove_html", "remove_urls", "remove_stopwords"]
preprocessor.clean_text(text, pipeline=pipeline)

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Testing

Run unit tests using:

python -m unittest discover tests

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License

This project is licensed under the MIT License.

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Contributing

Feel free to fork the repository, create a feature branch, and submit a pull request. Contributions are welcome!

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Snippets

Full Preprocessing Example

from kleantext.preprocessor import TextPreprocessor

# Initialize with default settings
preprocessor = TextPreprocessor(remove_stopwords=True, perform_spellcheck=False)

text = "Hello!!! This is, an example. Isn't it? 😊"
clean_text = preprocessor.clean_text(text)
print(clean_text)

Profanity Filtering

preprocessor = TextPreprocessor()
text = "This is a badword!"
clean_text = preprocessor.clean_text(text)
print(clean_text)  # Output: "This is a [CENSORED]!"