semvec/pypi-packages huggingface dataset:
hexdoc-hexbound 0.1.4.1.19.2.1.0.dev2
Python web book docgen and hexdoc plugin for Hexbound.
Requires Python: >=3.11
Keywords: hexdoc

# hexdoc-hexbound

Python web book docgen and [hexdoc](https://pypi.org/project/hexdoc) plugin for Hexbound.

## Version scheme

We use [hatch-gradle-version](https://pypi.org/project/hatch-gradle-version) to generate the version number based on whichever mod version the docgen was built with.

The version is in this format: `mod-version.python-version.mod-pre.python-dev.python-post`

For example:
* Mod version: `0.11.1-7`
* Python package version: `1.0.dev0`
* Full version: `0.11.1.1.0rc7.dev0`

## Setup

Install Python 3.11.

```sh
python3.11 -m venv venv

.\venv\Scripts\activate   # Windows
. venv/bin/activate.fish  # fish
source venv/bin/activate  # everything else

# run from the repo root, not doc/
pip install -e .[dev] --find-links ./doc/libs
```

## Usage

For local testing, create a file called `.env` in the repo root following this template:
```sh
GITHUB_REPOSITORY=object-Object/hexbound
GITHUB_SHA=1.19
GITHUB_PAGES_URL=https://hexbound.hexxy.media/
```

Useful commands:
```sh
# show help
hexdoc -h

# render and serve the web book in watch mode
nodemon --config doc/nodemon.json

# render and serve the web book
hexdoc serve

# export, render, and merge the web book
hexdoc export
hexdoc render
hexdoc merge

# start the Python interpreter with some extra local variables
hexdoc repl
```
aspect-library-v1 0.1.3
A Python library for aspect-based sentiment analysis with translation capabilities
Requires Python: >=3.6

# 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

Hereâ€™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.