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A Python library for aspect-based sentiment analysis with translation capabilities
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.
To install the package, you can simply clone the repository and use setup.py
to install it:
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:
pip install -e .
Here’s a quick example of how to use the library:
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
pandas
deep_translator
unlimited_machine_translator
pyabsa
nltk
These dependencies are automatically installed when you install the package.
This project is licensed under the MIT License - see the LICENSE file for details.
If you want to contribute to this project, feel free to fork the repository and submit a pull request.
Special thanks to all the contributors and maintainers of the libraries that this project depends on.
FAQs
A Python library for aspect-based sentiment analysis with translation capabilities
We found that aspect-library demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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