https://github.com/xga0/emoticon_fix
A lightweight and efficient library for transforming emoticons into their semantic meanings
https://github.com/xga0/emoticon_fix
emoticon emoticons meaningless-letters nlp nlp-model preprocessing punctuations python-package
Last synced: 3 months ago
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A lightweight and efficient library for transforming emoticons into their semantic meanings
- Host: GitHub
- URL: https://github.com/xga0/emoticon_fix
- Owner: xga0
- License: mit
- Created: 2020-08-06T22:36:55.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2025-08-12T01:43:41.000Z (10 months ago)
- Last Synced: 2026-01-03T03:58:54.652Z (5 months ago)
- Topics: emoticon, emoticons, meaningless-letters, nlp, nlp-model, preprocessing, punctuations, python-package
- Language: Python
- Homepage: https://pypi.org/project/emoticon-fix/
- Size: 127 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Emoticon Fix
[](https://pypi.org/project/emoticon-fix/)
[](https://pypi.org/project/emoticon-fix/)
[](https://opensource.org/licenses/MIT)
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.
## Quick Start
### Installation
```bash
pip install emoticon-fix
```
### Basic Usage
```python
from 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!'
```
## Features
- **Core Functionality**
- Transform emoticons to semantic meanings (`:)` → `Smile`)
- Support for Western emoticons and Japanese kaomoji
- Remove or replace emoticons with custom tags
- **Advanced Analytics**
- Sentiment analysis and scoring
- Emotion profiling and comparison
- Statistics and trend analysis
- Data export (JSON/CSV)
- **Text Processing Pipeline**
- Configurable, reusable workflows
- Built-in preprocessing steps
- Caching and batch processing
- Performance metrics and metadata
## More Options
```python
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__'
```
## Documentation
**[Complete Documentation](README_FULL.md)** - For comprehensive guides, advanced features, and detailed examples, see our full documentation.
### Key Features Covered:
- **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
## Contributing
Contributions are welcome! Please see the Contributing section in [README_FULL.md](README_FULL.md#contributing) for detailed guidelines.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.