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🎬 IMDB Review Sentiment Analyzer

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🚀 Project Overview

Welcome to IMDB Review Sentiment Analyzer, a sleek and user-friendly Streamlit app powered by a pretrained Recurrent Neural Network (RNN). It classifies movie reviews from the IMDB dataset as positive or negative — instantly, accurately, and with confidence scores.

Built with TensorFlow and Streamlit, this app showcases how to deploy NLP deep learning models for real-world applications, wrapped in a modern web UI.

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🔍 Key Features

• 🔮 Deep Learning with RNN: Robust sentiment classification using a pretrained LSTM model.
• 🧠 On-the-fly Preprocessing: Clean, tokenize, encode, and pad user inputs using the exact pipeline as training.
• 💻 Streamlit UI: Responsive, visually appealing interface with smooth animations and modern design.
• 📊 Confidence Scores: Real-time display of prediction probabilities for better transparency.
• 💾 Optimized Caching: Uses Streamlit’s @st.cache_resource decorator to reduce latency.
• 🎨 Modern UX: Gradient backgrounds, soft shadows, and responsive layouts ensure clarity and engagement.

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🛠️ Tech Stack

Layer	Tools / Libraries
Frontend	Streamlit, HTML/CSS, Custom CSS animations
Backend	TensorFlow, Keras, NumPy
Model	RNN with Embedding + LSTM layers (Pretrained)
Dataset	IMDB Movie Review Dataset (Top 10,000 words)

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📁 Project Structure

imdb-sentiment-analyzer/
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├── rnn_simple.h5           # Pretrained RNN model weights
├── app.py                  # Streamlit application script
├── requirements.txt        # Python package dependencies
└── README.md               # Project documentation (this file)

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## ⚙️ Installation & Usage

### ✅ Prerequisites

* Python 3.8 or higher
* `pip` or `virtualenv`

### 🔧 Installation Steps

```bash
# Clone the repository
git clone https://github.com/yourusername/imdb-sentiment-analyzer.git
cd imdb-sentiment-analyzer

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

🚀 Run the App

streamlit run app.py

Open your browser and visit [] to interact with the app.

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🧪 Testing the Model

Try these sample reviews to test the model’s prediction quality:

Positive Examples:

• “An absolutely amazing experience with a heartfelt story.”
• “Outstanding acting and direction — a must watch!”

Negative Examples:

• “Boring and predictable, a total waste of time.”
• “The script was weak and performances were flat.”

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⚠️ Limitations

• Vocabulary is limited to the top 10,000 most frequent words from the IMDB dataset.
• Misspelled, rare, or slang words may reduce prediction accuracy.
• Currently supports English reviews only.

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🌟 Future Enhancements

• Upgrade the model to transformer-based architectures (e.g., BERT, DistilBERT) for improved accuracy.
• Add multilingual support to handle reviews in different languages.
• Enable file upload functionality for batch review analysis (.txt, .csv).
• Implement user feedback collection and model retraining pipeline.
• Add deployment on cloud platforms for scalable access.

✨ Thank you for checking out this project! Feel free to ⭐ star and contribute.