Sentiment Analysis using RNN (PyTorch)

📌 Overview

This project implements a Recurrent Neural Network (RNN) for sentiment analysis on the IMDB movie reviews dataset using PyTorch. The model classifies reviews as positive or negative.

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🚀 Features

• Built using PyTorch
• Uses RNN architecture
• Trained on IMDB dataset
• Binary sentiment classification
• Preprocessing with tokenization and padding

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🧠 Model Architecture

• Embedding Layer
• RNN Layer
• Fully Connected Layer
• Sigmoid Activation

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📂 Project Structure

sentiment-rnn/
│
├── sentiment_rnn.ipynb   # Main notebook (Google Colab)
├── requirements.txt
├── .gitignore
└── README.md

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⚙️ Installation

Clone the repository:

git clone https://github.com/Fahad9009/Sentiment-analysis.git
cd sentiment-analysis

Install dependencies:

pip install -r requirements.txt

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▶️ Usage

Run the notebook:

• Open main.ipynb in Google Colab
• Execute all cells

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📊 Dataset

• IMDB Movie Reviews Dataset
• 50,000 reviews labeled as positive or negative

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📈 Results

• Model trained with RNN
• Achieved good accuracy on validation data (mention yours if needed)

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🛠️ Tech Stack

• Python
• PyTorch
• NumPy
• Pandas

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📄 License

MIT License