https://github.com/tanay-dwivedi/english-pip-package
Enalsis: Your go-to Python package for NLP tasks. Simplifying text analysis with entity recognition, sentiment analysis, parsing, and more.
https://github.com/tanay-dwivedi/english-pip-package
nlp pypi-package python
Last synced: 3 months ago
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Enalsis: Your go-to Python package for NLP tasks. Simplifying text analysis with entity recognition, sentiment analysis, parsing, and more.
- Host: GitHub
- URL: https://github.com/tanay-dwivedi/english-pip-package
- Owner: Tanay-Dwivedi
- License: mit
- Created: 2024-02-03T15:35:44.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-02-09T17:53:21.000Z (over 1 year ago)
- Last Synced: 2025-05-17T01:11:23.302Z (5 months ago)
- Topics: nlp, pypi-package, python
- Language: Python
- Homepage: https://enalsis-docs.vercel.app
- Size: 73.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
# English pip Package
-----
## Table of contents:
1. [Documentation](https://enalsis-docs.vercel.app)
2. [Usage](#usage)
3. [Aim](#aim)
4. [Advantages](#advantages)
5. [Disadvantages & Limitations](#disadvantages--limitations)
6. [Future Improvements](#future-improvements)
7. [Installation Instructions](#installation-instructions)
8. [Important files](#important-files)
-----
## Documentation:
Access comprehensive documentation for detailed information on using and integrating our package.
Access detailed information, helpful tips, and troubleshooting guidance by referring to our documentation on our [Website](https://enalsis-docs.vercel.app).
-----
## Usage:
To utilize the "enalsis" package in your Python programs, follow these simple steps:
1. **Installation**: Begin by installing the "enalsis" package. You can do this using pip, the Python package manager, by running the following command in your terminal or command prompt:
```bash
pip install enalsis
```
2. **Importing the Package**: Once installed, import the "enalsis" package into your Python script using the `import` statement:
```python
import enalsis
```
3. **Accessing and Utilizing Functions**: Once the package is imported, you can directly call its functions using dot notation. Provide the required arguments to the function as per its documentation. For example, to use the `get_entities` function to extract entities from text, you would call it like this:
```python
entities = enalsis.get_entities(text)
```
4. **Exploring Documentation**: For detailed information on each function and its parameters, refer to the package documentation. You can access the documentation online or directly from your Python environment using the `help` function:
```python
help(enalsis)
```
5. **Further Assistance**: If you encounter any issues or require assistance while using the "enalsis" package, refer to the package's documentation for troubleshooting tips and additional resources. You can also reach out to the package maintainers for support via [mail](tanaydwivedi2002@gmail.com).
By following these steps, you can seamlessly integrate the "enalsis" package into your Python programs and leverage its powerful functionalities for text analysis and exploration.
-----
## Aim:
The aim of the project is to provide a **comprehensive** Python **package** for **natural language processing (NLP)** tasks, facilitating **text analysis** and understanding through a range of functionalities. By offering functions for **entity recognition**, **sentiment analysis**, **syntactic parsing**, **spelling correction**, **readability assessment**, and more, the package aims to empower developers and researchers with efficient tools for **processing** and **analyzing textual data**.
Ultimately, the goal is to simplify the **implementation** of **NLP tasks** within **Python projects**, enabling users to extract **valuable insights** from text data with ease and accuracy.
-----
## Advantages:
1. **Versatile Functionality**: The project offers a wide array of text analysis tools covering entity recognition, sentiment analysis, syntactic parsing, and more.
2. **User-Friendly Interface**: With clear documentation and an intuitive API, users can easily integrate and utilize the package in their projects.
3. **Reliable Results**: Leveraging established libraries like spaCy and TextBlob ensures accurate and dependable text analysis outcomes.
4. **Customizable**: Users have the flexibility to adjust parameters and fine-tune algorithms to meet specific project requirements.
5. **Contributing to NLP**: By facilitating advancements in natural language processing research, the project plays a role in driving innovation and progress in the field.
-----
## Disadvantages & Limitations:
1. **Continuous Improvement**: Ongoing enhancements are necessary for evolving needs, but resource allocation and prioritization may pose challenges.
2. **Customization Constraints**: Addressing highly specialized requirements may require extra effort or external integration, potentially limiting adaptability.
3. **Integration Challenges**: Integrating with emerging technologies may face obstacles like compatibility issues or resource constraints, slowing down progress.
4. **Community Complexities**: Diverse contributions may introduce complexities like conflicting priorities, requiring effective community management.
5. **Learning Curve**: Users may face challenges with the complexity of NLP concepts or contributing to open-source, emphasizing the need for accessible documentation.
-----
## Future Improvements:
Certainly, here are five points on potential future enhancements:
1. **Advanced Machine Learning Models**: Integration of state-of-the-art machine learning models, such as transformer-based architectures like BERT or GPT, to improve the accuracy and performance of text analysis tasks.
2. **Multilingual Support**: Expansion of language support beyond English to include a broader range of languages, catering to diverse linguistic datasets and enabling global applications.
3. **Real-time Processing**: Development of real-time processing capabilities to handle streaming data, facilitating dynamic text analysis tasks in applications like social media monitoring or live chat support.
4. **Interactive Visualization**: Implementation of interactive visualization tools to provide intuitive visual representations of text analysis results, enhancing user understanding and exploration of textual data.
5. **Privacy and Security Features**: Integration of enhanced privacy and security features to ensure compliance with data protection regulations and safeguard sensitive information during text analysis processes.
### Additional features to add
- Perform Hate Speech Analysis and Detection on Text
- Perform Topic Modeling on the text
- Intent Recognition of the text
-----
## Installation Instructions:
### Step 1: Install Python
If you haven't already, you need to install Python on your system. You can download Python from the official website: [Python Downloads](https://www.python.org/downloads/).
### Step 2: Install an Integrated Development Environment (IDE)
Choose an IDE to work with Python. Some popular options include:
- [PyCharm](https://www.jetbrains.com/pycharm/)
- [Visual Studio Code (VSCode)](https://code.visualstudio.com/)
- [Spyder](https://www.spyder-ide.org/)
- [Jupyter Notebook](https://jupyter.org/install)
Download and install your preferred IDE on your system.
### Step 3: Install Git (Optional but Recommended)
If you want to clone the project directly from GitHub, you need to install Git. You can download Git from the official website: [Git Downloads](https://git-scm.com/downloads).
### Step 4: Clone Your Project Repository (Optional)
If you prefer to work with the project locally, you can clone the project repository to your machine using Git. Open a terminal or command prompt and navigate to the directory where you want to clone the repository. Then run:
```bash
git clone https://github.com/Tanay-Dwivedi/English-pip-Package.git
```
### Step 5: Navigate to the Project Directory
Open your IDE and navigate to the directory where your project is located.
### Step 6: Install Your Package Dependencies
To install the required dependencies for your package to run, open a terminal or command prompt within your IDE and navigate to the project directory. Then run:
```bash
pip install -r requirements.txt
```
This command will install all the required dependencies listed in the `requirements.txt` file, ensuring your package can function properly.
### Step 7: Use Your Package
You can now use your package in your Python scripts. Import it using:
```python
import enalsis
```
Then, you can call the functions provided by your package as needed.
### Additional Notes:
- Ensure that your package's dependencies are listed in a `requirements.txt` file or clearly documented in your project's README.
- Refer to your package's documentation for detailed usage instructions and examples.
- Provide a link to your package's GitHub repository or any other relevant resources for further exploration and support.
-----
## Important files:
### Contributing File
- **You can contribute**: We welcome contributions to enhance our project. Check out our [Contributing Guidelines](https://github.com/Tanay-Dwivedi/English-pip-Package/blob/master/CONTRIBUTING.md) to learn how you can get involved, submit bug fixes, suggest new features, and collaborate with us.
### Code of Conduct File
- **You must adhere**: We maintain a friendly and inclusive environment for all contributors and users. Before participating in our community, please review our [Code of Conduct](https://github.com/Tanay-Dwivedi/English-pip-Package/blob/master/CODE_OF_CONDUCT.md) to understand the behavior we expect and the consequences of unacceptable conduct.
### Bug Report File
- **You can report**: Encountered a bug? Let us know! Follow our [Bug Reporting Guidelines](https://github.com/Tanay-Dwivedi/English-pip-Package/blob/master/.github/ISSUE_TEMPLATE/bug_report.md) to provide detailed information about the issue, including steps to reproduce it. Your reports help us improve the project for everyone.
### Request Feature File
- **You can request**: Have an idea for a new feature or enhancement? Share it with us! Check out our [Feature Request Guidelines](https://github.com/Tanay-Dwivedi/English-pip-Package/blob/master/.github/ISSUE_TEMPLATE/feature_request.md) to learn how you can request new features and provide valuable insights into how the project can better meet your needs.
### Changelog File
- **You can track**: Stay up-to-date with our project's changes and improvements. Our [Changelog](https://github.com/Tanay-Dwivedi/English-pip-Package/blob/master/CHANGELOG.md) documents each version's release notes, highlighting new features, bug fixes, and other modifications. It's your guide to what's new and improved.
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