A lightweight and efficient library for transforming emoticons into their semantic meanings. Perfect for NLP preprocessing where emoticons need to be preserved as meaningful text.
pip install emoticon-fixfrom emoticon_fix import emoticon_fix
# Transform emoticons to meaningful text
text = 'Hello :) World :D'
result = emoticon_fix(text)
print(result) # Output: 'Hello Smile World Laugh'
# Works with kaomoji too!
text = 'Feeling (｡♥‿♥｡) today!'
result = emoticon_fix(text)
print(result) # Output: 'Feeling In Love today!'-
Core Functionality
- Transform emoticons to semantic meanings (
:)→Smile) - Support for Western emoticons and Japanese kaomoji
- Remove or replace emoticons with custom tags
- Transform emoticons to semantic meanings (
-
Advanced Analytics
- Sentiment analysis and scoring
- Emotion profiling and comparison
- Statistics and trend analysis
- Data export (JSON/CSV)
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Text Processing Pipeline
- Configurable, reusable workflows
- Built-in preprocessing steps
- Caching and batch processing
- Performance metrics and metadata
from emoticon_fix import remove_emoticons, replace_emoticons
# Remove emoticons completely
clean_text = remove_emoticons('Hello :) World :D')
print(clean_text) # Output: 'Hello World'
# Replace with NER-friendly tags
tagged_text = replace_emoticons('Hello :) World :D', tag_format="__EMO_{tag}__")
print(tagged_text) # Output: 'Hello __EMO_Smile__ World __EMO_Laugh__'Complete Documentation - For comprehensive guides, advanced features, and detailed examples, see our full documentation.
- Sentiment Analysis - Advanced emotion detection and scoring
- Analytics & Statistics - Comprehensive emoticon usage analysis
- Text Processing Pipeline - Configurable, reusable workflows
- Data Export - JSON/CSV export capabilities
- Real-world Examples - Social media analysis, customer feedback, and more
Contributions are welcome! Please see the Contributing section in README_FULL.md for detailed guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.