Sentimentify - Spotify Review Sentiment Analyzer

📊 Overview

Sentimentify is an AI-powered sentiment analysis application designed to analyze Spotify user reviews. Using a fine-tuned DistilBERT transformer model, it classifies reviews as positive, neutral, or negative while providing confidence scores and visualizations.

✨ Features

• Real-time sentiment analysis of user-entered reviews
• Interactive UI with Spotify-inspired design
• Review history tracking with persistence
• Detailed model metrics and performance visualizations
• Auto-suggestion for text input
• Mobile-responsive design

🧠 Model Performance

The DistilBERT model powering Sentimentify achieves:

• 86.17% validation accuracy
• 0.8622 F1 score
• Balanced performance across sentiment classes

🛠️ Tech Stack

• Frontend: HTML, CSS, JavaScript, Tailwind CSS
• Backend: Python, Flask
• ML Model: DistilBERT (PyTorch), Transformers, NLTK
• Deployment: GitHub Pages (frontend), Cloud provider (backend)

🚀 Deployment Guide

Prerequisites

• Python 3.8+
• Node.js (optional, for local development)
• Git

Frontend Deployment (GitHub Pages)

1. Fork or clone this repository
2. Enable GitHub Pages in your repository settings:
   • Go to Settings > Pages
   • Select the main branch as source
   • Save

Backend Deployment (e.g., Render)

1. Create a requirements.txt file in your repository (already included): torch transformers nltk emoji flask flask-cors joblib gunicorn
2. Create a free account on Render
3. Create a new Web Service and link to your GitHub repo
4. Configure the service:

• Build command: pip install -r requirements.txt
• Start command: gunicorn sentiment_api:app
• Environment variables: Add any necessary API keys

5. Deploy the service

Connect Frontend to Backend

1. Update the API endpoint in script.js:

// Change from local to deployed URL
const response = await fetch('https://your-backend-name.onrender.com/analyze', {
    // ...
});

🧪 Local Development

1. Clone the repository

git clone https://github.com/yourusername/sentimentify.git
cd sentimentify

2. Initialize Virtual Environment and Install backend dependencies

python -m venv venv_sentimental
.\venv_sentimental\Scripts\activate
pip install -r requirements.txt

3. Run the Flask API

python sentiment_api.py

4. Open index.html in your browser or use a local server

# Using Python's built-in server
python -m http.server

📚 Model Details

Base Model: DistilBERT (distilbert-base-uncased) Parameters: 66M Layers: 6 transformer blocks Embedding Size: 768 Training: 10 epochs (early stopping at epoch 9) Performance: 86.17% validation accuracy, 0.8622 F1 score

🤝 Contributing

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

📄 License This project is licensed under the MIT License - see the LICENSE file for details. ```