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https://github.com/avirsaha/documind
DocuMind - Python project for document analysis. Analyze, summarize, and determine sentiment in text and word documents. Supports .txt and .docx formats. Unlock the power of document analysis!
https://github.com/avirsaha/documind
natural-language-processing python-application
Last synced: 7 days ago
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DocuMind - Python project for document analysis. Analyze, summarize, and determine sentiment in text and word documents. Supports .txt and .docx formats. Unlock the power of document analysis!
- Host: GitHub
- URL: https://github.com/avirsaha/documind
- Owner: avirsaha
- License: mit
- Created: 2023-06-23T12:17:06.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-09-11T15:30:06.000Z (2 months ago)
- Last Synced: 2024-09-12T00:04:05.719Z (2 months ago)
- Topics: natural-language-processing, python-application
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DocuMind - Document Analysis Module
DocuMind is a document analysis module that provides functionality to analyze text documents, including summarization and sentiment analysis.
**Author**: Aviraj Saha
**Copyright**: ©Aviraj Saha 2023
## Table of Contents
- [Overview](#overview)
- [Installation](#installation)
- [Usage](#usage)
- [Example](#example)
- [Contributing](#contributing)
- [License](#license)
## Overview
The module consists of three main files:
- `main.py`: This file is the entry point of the DocuMind document analysis module. It provides a command-line interface for analyzing text documents. It uses the `doc_analyzer` module to perform document analysis and outputs the results to a file.
- `doc_analyzer.py`: This file contains the `Document`, `TextDocument`, and `WordDocument` classes that encapsulate the functionality for document summarization and sentiment analysis. It uses the Sumy library for text summarization and the TextBlob library for sentiment analysis.
- `cmd_utility.py`: This file contains the command-line utility functions for parsing command-line arguments, handling logging, and calling the `output` function in `main.py`.
## Installation
1. Clone this repository to your local machine:
```bash
git clone https://github.com/Aviraj06/DocuMind.git
```
2. Navigate to the project directory:
```bash
cd DocuMind
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
To analyze a text document using DocuMind, follow these steps:
1. Open a terminal or command prompt and navigate to the project directory.
2. Run the `main.py` script:
```bash
python main.py
```
3. You will be prompted to enter the path to the input document (.txt or .docx), the output path, and the number of sentences for the summary.
4. The script will analyze the document and generate an output file with the summarized text and sentiment analysis results.
## Example
```shell
python main.py
```
**Sample Output:**
```
DocuMind - A document analysis module.
This module provides functionality to analyze text documents, including summarization and sentiment analysis.
Author: Aviraj Saha
Copyright: ©Aviraj Saha 2023
Enter a valid file path for .txt or a .docx file: path/to/input/document.txt
Enter path for output: path/to/output/
Enter number of lines for summary: 3
Loading: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:03<00:00, 32.11it/s]
Process finished successfully!
Results saved at: path/to/output/document_documind_output.txt
```
## Contributing
Contributions to DocuMind are welcome! If you would like to contribute to this project, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature/bug fix:
```bash
git checkout -b feature/your-feature
```
3. Make your changes and commit them:
```bash
git commit -m "Add your commit message"
```
4. Push your changes to your forked repository:
```bash
git push origin feature/your-feature
```
5. Open a pull request on the original repository.
## License
This project is licensed under the [MIT License](LICENSE).
```