https://github.com/mohammadreza-mohammadi94/text-analysis-using-nlp-streamlit
This project is a Streamlit-based NLP application that performs text analysis. It allows users to input text, analyze tokens, extract entities, find common tokens, and assess sentiment. The interface includes various expandable sections for detailed insights and visualizations.
https://github.com/mohammadreza-mohammadi94/text-analysis-using-nlp-streamlit
natural-language-processing nlp pandas spacy-nlp streamlit
Last synced: 3 days ago
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This project is a Streamlit-based NLP application that performs text analysis. It allows users to input text, analyze tokens, extract entities, find common tokens, and assess sentiment. The interface includes various expandable sections for detailed insights and visualizations.
- Host: GitHub
- URL: https://github.com/mohammadreza-mohammadi94/text-analysis-using-nlp-streamlit
- Owner: mohammadreza-mohammadi94
- License: mit
- Created: 2024-07-21T09:42:58.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-07-21T16:19:03.000Z (10 months ago)
- Last Synced: 2025-05-16T11:09:26.491Z (3 days ago)
- Topics: natural-language-processing, nlp, pandas, spacy-nlp, streamlit
- Language: Python
- Homepage:
- Size: 52.7 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
- License: LICENSE.txt
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README
# Text Analysis Using NLP with Streamlit
Welcome to the Text Analysis Using NLP with Streamlit repository. This project demonstrates how to leverage Natural Language Processing (NLP) techniques to perform various text analysis tasks, integrated into an interactive web application using Streamlit.
## Overview
This repository provides a comprehensive text analysis tool that includes functionalities such as text preprocessing, sentiment analysis, named entity recognition, and more. The web application is built with Streamlit, making it easy to deploy and interact with various NLP models and techniques.
## Features
- **Text Preprocessing:** Clean and preprocess text data by removing stopwords, punctuation, and applying tokenization.
- **Sentiment Analysis:** Analyze the sentiment of the input text (positive, negative, neutral) using pre-trained models.
- **Named Entity Recognition (NER):** Identify and classify named entities (e.g., persons, organizations, locations) in the text.
- **Word Cloud Generation:** Visualize the most frequent words in the text data using word clouds.
- **Part-of-Speech Tagging:** Tag each word in the text with its respective part-of-speech.
- **Customizable Parameters:** Easily adjust parameters for different NLP tasks through the Streamlit interface.
## Getting Started
### Prerequisites
Ensure you have the following installed on your machine:
- Python 3.6 or higher
- pip (Python package installer)
### Installation
1. **Clone the repository:**
```bash
git clone https://github.com/mohammadreza-mohammadi94/Text_Analysis_Using_NLP_Streamlit.git
cd Text_Analysis_Using_NLP_Streamlit
```
2. **Create a virtual environment:**
```bash
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. **Install the required packages:**
```bash
pip install -r requirements.txt
```
### Running the Application
To run the Streamlit application, use the following command:
```bash
streamlit run app.py
```
This will start a local web server and open the application in your default web browser.
## Project Structure
- `app.py`: Main application file containing the Streamlit interface and NLP functionalities.
- `requirements.txt`: List of required Python packages.
- `utils.py`: Utility functions for text preprocessing and NLP tasks.
- `README.md`: Project documentation.
## Usage
Once the application is running, you can perform various NLP tasks by entering text into the input field and selecting the desired analysis options. The results will be displayed interactively on the web page.
## Example
Here's a brief example of how to use the application:
1. Open the application by running `streamlit run app.py`.
2. Enter the text you want to analyze in the input field.
3. Select the desired NLP tasks such as Sentiment Analysis, Named Entity Recognition, etc.
4. View the results displayed interactively on the web page.
## Contributing
Contributions are welcome! If you have any ideas for improvements or new features, feel free to open an issue or submit a pull request.
### Steps to Contribute
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add new feature'`).
5. Push to the branch (`git push origin feature-branch`).
6. Open a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Acknowledgements
- [Streamlit](https://www.streamlit.io/) for providing an easy-to-use framework for building web applications.
- [spaCy](https://spacy.io/) for providing powerful NLP tools and models.
## Contact
For any questions or feedback, please feel free to reach out via GitHub issues.