🎬 IMDB Movie Review Sentiment Analysis with Simple RNN

🔴 Live Demo: Click here to try the App

A Deep Learning project that performs sentiment analysis on movie reviews using the IMDB dataset. This project utilizes a Simple RNN (Recurrent Neural Network) built with TensorFlow/Keras and features a user-friendly web interface deployed using Streamlit.

🚀 Project Overview

The goal of this project is to classify movie reviews as either Positive or Negative. The model processes natural language text, converting words into integer sequences, and uses a Recurrent Neural Network to understand the context and sentiment of the review.

✨ Key Features

• Deep Learning Model: Built using a Simple RNN architecture suitable for sequential text data.
• Dataset: Trained on the standard IMDB dataset (25,000 training reviews).
• Text Preprocessing: Implements tokenization, decoding, and sequence padding to handle varying review lengths.
• Visualization: Utilizes TensorBoard to track training metrics (loss and accuracy) over epochs.
• Deployment: Includes a Streamlit web application for real-time, interactive sentiment prediction.

🛠️ Tech Stack

• Python
• TensorFlow / Keras (Model building & training)
• NumPy (Data manipulation)
• Streamlit (Web interface)
• TensorBoard (Performance visualization)

📂 Project Structure

File Name	Description
IMDB_rnn.ipynb	Jupyter Notebook for data loading, preprocessing, model training, and saving the model.
main.py	The Streamlit application script that loads the trained model and creates the web interface.
prediction.ipynb	A testing notebook to load the model and run predictions on raw text examples.
simple_rnn_imdb.h5	The saved pre-trained model file generated after training.
requirements.txt	List of dependencies required to run the project.
logs/	Directory containing TensorBoard logs for visualization.

💻 Installation & Setup

1. Clone the repository

git clone [https://github.com/yourusername/imdb-sentiment-analysis.git](https://github.com/yourusername/imdb-sentiment-analysis.git)
cd imdb-sentiment-analysis

2. Create a Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

Install the required libraries using the requirements file:

pip install -r requirements.txt

Note: Your requirements.txt should typically include:

• numpy
• tensorflow
• streamlit

🚀 How to Run

1. Train the Model (Optional)

If you want to retrain the model from scratch, run the IMDB_rnn.ipynb notebook. This will generate the simple_rnn_imdb.h5 file and TensorBoard logs.

2. Visualize Training with TensorBoard

To view the training graphs (Accuracy vs. Loss):

tensorboard --logdir=logs/fit

3. Launch the Web App

Run the Streamlit application to use the model interactively:

streamlit run main.py

Once the app is running, enter any movie review in the text box and click Classify to see if the sentiment is Positive or Negative.

📊 Model Architecture

The model consists of the following layers:

1. Embedding Layer: Converts integer-encoded words into dense vectors of fixed size (128).
2. SimpleRNN Layer: A recurrent layer with 128 units (ReLU activation) to capture sequential patterns.
3. Dense Layer: A single output unit with Sigmoid activation to output a probability score (0 to 1).

📈 Results

The model achieves a validation accuracy of approximately 83% on the IMDB dataset.

Created by Ritee