https://github.com/ravirch/sentiment-analysis-with-rnn
An end-to-end sentiment analysis project using a Recurrent Neural Network (RNN) with a Streamlit app for real-time predictions.
https://github.com/ravirch/sentiment-analysis-with-rnn
rnn-tensorflow sentiment-analysis
Last synced: 7 months ago
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An end-to-end sentiment analysis project using a Recurrent Neural Network (RNN) with a Streamlit app for real-time predictions.
- Host: GitHub
- URL: https://github.com/ravirch/sentiment-analysis-with-rnn
- Owner: ravirch
- Created: 2024-11-12T10:12:35.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T10:21:31.000Z (11 months ago)
- Last Synced: 2025-01-22T06:36:43.537Z (9 months ago)
- Topics: rnn-tensorflow, sentiment-analysis
- Language: Jupyter Notebook
- Homepage: https://sentiment-analysis-with-rnn-ravirch.streamlit.app/
- Size: 13.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# Sentiment Analysis with RNN
This repository contains an end-to-end solution for performing sentiment analysis using a Recurrent Neural Network (RNN). It includes a trained model, Jupyter notebooks for model development, and a Streamlit app for interactive sentiment prediction.
## Project Structure
- **Model**: Contains the pre-trained RNN model saved for quick use in predictions.
- **Notebooks**: Jupyter notebooks that walk through the data exploration, preprocessing, and training steps for building the sentiment analysis model.
- **main.py**: A Streamlit app that allows users to input text and receive a sentiment analysis prediction in real time.
- **requirements.txt**: List of dependencies required to run the notebooks and the Streamlit app.
## Setup and Installation
1. **Clone the repository**:
```bash
git clone https://github.com/ravirch/Sentiment-Analysis-with-RNN.git
cd Sentiment-Analysis-with-RNN
```
2. **Install dependencies**:
Make sure you have Python installed, then install the required packages:
```bash
pip install -r requirements.txt
```
## How to Use
### Running the Streamlit App
The `main.py` file is a Streamlit app that provides an interactive interface for sentiment prediction.
To launch the app, run:
```bash
streamlit run main.py
```
Once running, you can input text, and the app will return the predicted sentiment based on the trained RNN model.
### Jupyter Notebooks
The **Notebooks** folder includes Jupyter notebooks that guide you through the entire process of data exploration, preprocessing, and training the RNN model for sentiment analysis.
To open a notebook:
```bash
jupyter notebook Notebooks/.ipynb
```
## Model Information
The RNN model is trained on a dataset of text samples and is designed to classify the sentiment of each input. This setup is suitable for analyzing sentiments in user-generated content, such as reviews or social media posts.
## Contributing
Contributions are welcome! If you find any issues or have suggestions, please open an issue or submit a pull request.
## Acknowledgments
This project was inspired by Krish Naik's course on Generative AI. Special thanks to Krish Naik for his insights and guidance in creating this model.
---