🎥 Review Sentiment Classifier

This project uses a Recurrent Neural Network (RNN) model to classify movie reviews as Positive or Negative. The model is trained on the IMDB dataset and deployed using Streamlit for an interactive web interface.

Deployed at :

🛠 Features

• Model Training: A simple RNN model is trained on the IMDB dataset (simplernn.ipynb).
• Prediction Script: A script to test the trained model with new reviews (prediction.ipynb).
• Web Interface: A user-friendly Streamlit app to classify movie reviews (app.py).
• Pre-trained Model: Includes a pre-trained RNN model (rnn_model_imdb.h5).

📁 Project Structure

.
├── app.py                 # Streamlit app for user interaction
├── simplernn.ipynb        # Notebook for training the RNN model
├── prediction.ipynb       # Notebook for testing predictions
├── rnn_model_imdb.h5      # Pre-trained RNN model
├── requirements.txt       # Dependencies required to run the project
└── README.md              # Project documentation

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/VanshajR/RNN-Review-Classification.git
   cd RNN-Review-Classification
   

2. Install the required packages:

   pip install -r requirements.txt
   

3. Run the simplernn.ipynb notebook to train and save the model

4. Run the Streamlit app:

   streamlit run app.py
   

Usage

1. Open the app in your browser (usually at http://localhost:8501).
2. Enter a movie review in the text box.
3. Click the Classify Sentiment button to get the prediction result.

📊 Model Training

The RNN model was trained using TensorFlow on the IMDB dataset. The training process is documented in simplernn.ipynb.

Built With

• jupyter-notebook
• python