# My Aspect Library
## Overview
My Aspect Library is a Python package designed for performing aspect-based sentiment analysis with integrated translation capabilities. This library allows you to easily translate text, extract aspects, and analyze sentiment, making it a powerful tool for natural language processing tasks.
## Features
- **Translation**: Automatically translate text in your dataset to the target language before analysis.
- **Aspect Extraction**: Extract aspect terms from text using state-of-the-art models.
- **Sentiment Analysis**: Analyze sentiment associated with extracted aspects.
- **Data Processing**: Clean and process text data for analysis, including stopword removal and text normalization.
- **Pivot Table Generation**: Create pivot tables to summarize sentiment analysis results.
## Installation
To install the package, you can simply clone the repository and use `setup.py` to install it:
```bash
git clone https://github.com/yourusername/my_aspect_library.git
cd my_aspect_library
pip install .
```
Alternatively, if you want to install it in editable mode:
```bash
pip install -e .
```
## Usage
Here’s a quick example of how to use the library:
```python
import pandas as pd
from my_aspect_library import AspectExtractor, translate_aspects, create_pivot_table, concatenate_results
# Load your dataset
df = pd.read_excel('path_to_your_file.xlsx')
# Initialize the aspect extractor
aspect_extractor = AspectExtractor()
# Perform translation and aspect extraction in one step
result_df = aspect_extractor.extract(df, column_name='Customer Comments', target_language='en')
# Translate aspects and sentiments
translated_aspects = translate_aspects(result_df)
# Create pivot table for sentiment analysis
pivot_table = create_pivot_table(translated_aspects)
# Save or further process your results as needed
```
## Dependencies
- `pandas`
- `deep_translator`
- `unlimited_machine_translator`
- `pyabsa`
- `nltk`
These dependencies are automatically installed when you install the package.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Contributing
If you want to contribute to this project, feel free to fork the repository and submit a pull request.
## Acknowledgments
Special thanks to all the contributors and maintainers of the libraries that this project depends on.
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"description": "# My Aspect Library\r\n\r\n## Overview\r\n\r\nMy Aspect Library is a Python package designed for performing aspect-based sentiment analysis with integrated translation capabilities. This library allows you to easily translate text, extract aspects, and analyze sentiment, making it a powerful tool for natural language processing tasks.\r\n\r\n## Features\r\n\r\n- **Translation**: Automatically translate text in your dataset to the target language before analysis.\r\n- **Aspect Extraction**: Extract aspect terms from text using state-of-the-art models.\r\n- **Sentiment Analysis**: Analyze sentiment associated with extracted aspects.\r\n- **Data Processing**: Clean and process text data for analysis, including stopword removal and text normalization.\r\n- **Pivot Table Generation**: Create pivot tables to summarize sentiment analysis results.\r\n\r\n## Installation\r\n\r\nTo install the package, you can simply clone the repository and use `setup.py` to install it:\r\n\r\n```bash\r\ngit clone https://github.com/yourusername/my_aspect_library.git\r\ncd my_aspect_library\r\npip install .\r\n```\r\n\r\nAlternatively, if you want to install it in editable mode:\r\n\r\n```bash\r\npip install -e .\r\n```\r\n\r\n## Usage\r\n\r\nHere\u00e2\u20ac\u2122s a quick example of how to use the library:\r\n\r\n```python\r\nimport pandas as pd\r\nfrom my_aspect_library import AspectExtractor, translate_aspects, create_pivot_table, concatenate_results\r\n\r\n# Load your dataset\r\ndf = pd.read_excel('path_to_your_file.xlsx')\r\n\r\n# Initialize the aspect extractor\r\naspect_extractor = AspectExtractor()\r\n\r\n# Perform translation and aspect extraction in one step\r\nresult_df = aspect_extractor.extract(df, column_name='Customer Comments', target_language='en')\r\n\r\n# Translate aspects and sentiments\r\ntranslated_aspects = translate_aspects(result_df)\r\n\r\n# Create pivot table for sentiment analysis\r\npivot_table = create_pivot_table(translated_aspects)\r\n\r\n# Save or further process your results as needed\r\n```\r\n\r\n## Dependencies\r\n\r\n- `pandas`\r\n- `deep_translator`\r\n- `unlimited_machine_translator`\r\n- `pyabsa`\r\n- `nltk`\r\n\r\nThese dependencies are automatically installed when you install the package.\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n\r\n## Contributing\r\n\r\nIf you want to contribute to this project, feel free to fork the repository and submit a pull request.\r\n\r\n## Acknowledgments\r\n\r\nSpecial thanks to all the contributors and maintainers of the libraries that this project depends on.\r\n",
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