📚 Simple RNN-Based IMDB Movie Review Sentiment Analysis

A deep learning project utilizing a Recurrent Neural Network (RNN) to classify IMDB movie reviews as positive or negative, featuring an interactive Streamlit web application.

🚀 Overview

This project implements a simple RNN model to perform sentiment analysis on IMDB movie reviews. The model is trained to predict whether a given movie review expresses a positive or negative sentiment. An interactive user interface is provided through Streamlit, allowing users to input their own reviews and receive real-time sentiment predictions.

🛠️ Technologies Used

• TensorFlow/Keras: For building and training the RNN model.
• Pandas & NumPy: For data processing and manipulation.
• Streamlit: For creating an interactive web application.
• Jupyter Notebook: For interactive and flexible environment.

🌐 Live Demo

Try the live demo here: Live App

📂 Project Structure

• main.py: Streamlit application script.
• simplernn.h5: Pre-trained RNN model file.
• requirements.txt: List of required Python packages.
• README.md: Project documentation (this file).
• img1.png & img2.png: Sample images from the project.

🛠️ Setup and Installation

1. Clone the Repository

   git clone https://github.com/laavanjan/Simple_RNN_imdb.git
   cd Simple_RNN_imdb

2. Create and Activate a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

🏃‍♂️ Usage

1. Run the Streamlit App

   streamlit run main.py

2. Interact with the App

   • Open your browser and navigate to http://localhost:8501.
   • Enter a movie review in the text box provided.
   • Click the "Classify" button to see the predicted sentiment and confidence score.

📊 Model Training

The RNN model was trained on the IMDB dataset, which contains 25,000 movie reviews labeled as positive or negative. The training process involved:

• Tokenizing and padding the text data.
• Building a Sequential model with an Embedding layer and a SimpleRNN layer.
• Compiling the model with the Adam optimizer and binary cross-entropy loss function.
• Training the model over multiple epochs with a batch size of 32.

📈 Performance

The model achieves an accuracy of approximately 85% on the test dataset, demonstrating its effectiveness in classifying movie review sentiments.

📷 Screenshots

Sample images from the project:

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the GPL License.

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