https://github.com/amarnarayandwivedi/rnn-movie-sentiment-analysis
This project leverages Recurrent Neural Networks (RNNs) to predict the sentiment (positive or negative) of movie reviews.
https://github.com/amarnarayandwivedi/rnn-movie-sentiment-analysis
python rnn streamlit tensorflow
Last synced: 7 months ago
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This project leverages Recurrent Neural Networks (RNNs) to predict the sentiment (positive or negative) of movie reviews.
- Host: GitHub
- URL: https://github.com/amarnarayandwivedi/rnn-movie-sentiment-analysis
- Owner: AmarNarayanDwivedi
- License: gpl-3.0
- Created: 2025-01-10T14:20:53.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-10T14:50:28.000Z (9 months ago)
- Last Synced: 2025-01-10T15:38:13.689Z (9 months ago)
- Topics: python, rnn, streamlit, tensorflow
- Language: Jupyter Notebook
- Homepage: https://rnn-movie-sentiment-analysis.streamlit.app/
- Size: 13.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RNN Movie Sentiment Analysis
## Overview
**RNN Movie Sentiment Analysis** is a project that uses Recurrent Neural Networks (RNNs) to analyze and predict the sentiment of movie reviews. The model classifies reviews into two categories: positive or negative. This project leverages the IMDB movie review dataset to train the RNN and evaluate the sentiment of textual data effectively.
## Features
- Sentiment classification: Predicts whether a movie review is positive or negative.
- Utilizes Recurrent Neural Networks (RNNs) to process sequential data.
- Trained on the IMDB dataset with over 25,000 reviews.
- Achieves high accuracy in sentiment prediction.
## Installation
To set up the project locally, follow these steps:
1. Clone this repository:
```bash
git clone https://github.com/your-username/rnn-movie-sentiment-analysis.git
```
2. Navigate to the project directory:
```bash
cd rnn-movie-sentiment-analysis
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Dataset
The project uses the IMDB Movie Review Dataset, which contains 25,000 reviews for training and 25,000 for testing. The dataset is preprocessed to convert the text into a numerical format suitable for input to the RNN model.
## Model Architecture
The model uses an Embedding Layer for text representation, followed by a SimpleRNN layer to capture sequential dependencies in the review data. Finally, a Dense output layer predicts the sentiment (positive or negative) of each review.
## Contributing
Feel free to open issues or submit pull requests if you'd like to contribute to improving this project!
### Key Sections Explained:
- **Overview**: Brief explanation of the project and its goals.
- **Features**: Highlights the key functionalities of the project.
- **Installation**: Step-by-step guide for setting up the project.
- **Dataset**: Information about the dataset being used.
- **Model Architecture**: A short explanation of the neural network layers used.
- **Contributing**: Encourages others to contribute to the project.
This README provides a clear introduction to the project and how others can use or contrib