💬 Sentiment Analysis using Simple RNN:

A deep learning-based sentiment classification project that predicts whether a review is Positive or Negative using a Simple RNN architecture. The project includes an embedding layer, and features a user-friendly Streamlit web app to input and test reviews interactively.

🚀 Project Highlights:

✅ Built using TensorFlow/Keras with Simple RNN architecture

🔤 Includes Embedding Layer to handle text inputs

🌐 Web app interface built using Streamlit for real-time predictions

📊 Trained on labeled review dataset (IMDb/Amazon/etc.)

📦 Easy to run locally with a clean, modular codebase

🧠 Model Architecture:

Input Text → Tokenizer → Embedding Layer → Simple RNN → Dense → Output (Positive/Negative) Embedding Layer: Converts text to dense vector representation

Simple RNN: Learns sequential patterns from the reviews

Dense Layer: Final binary classification (sigmoid activation)

🖥️ Web Application (Streamlit UI):

The web app allows users to:

• Enter a custom review

• Click to analyze

• Instantly view whether the review is Positive 😊 or Negative 😞

📸 Screenshots:

🧪 Tech Stack:

Python 3.11

TensorFlow / Keras

NumPy, Pandas

Streamlit

⚙️ Setup Instructions:

• 🐍 1. Clone the Repo

• git clone https://github.com/Shiva-Prasad-Naroju/Review-Sentimental-Analysis-using-SimpleRNN.git

• cd Review-Sentimental-Analysis-using-SimpleRNN

• 📦 2. Create & Activate Virtual Environment (Optional but recommended)

• python -m venv venv

Windows

venv\Scripts\activate

macOS/Linux

source venv/bin/activate

• 🔧 3. Install Dependencies

• pip install -r requirements.txt

• 🧠 4. Run the App

• streamlit run app.py

📝 Future Enhancements:

Switch to LSTM/GRU for improved performance

Add visual analytics to display confidence scores

Train on larger or domain-specific datasets