📊 Social Pulse Analyzer

AI-Powered Social Media Virality & Sentiment Intelligence Platform

Social Pulse Analyzer is a full-stack AI-powered web application that analyzes social media post virality and sentiment, suggests optimized captions and hashtags, and generates audio summaries in Hindi and English.

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🚀 Live Web App

🔗 Access the deployed application here:
https://socialpulseanalyzer.onrender.com

Hosted on Render (Production Deployment)

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🎯 Problem Statement

Content creators and brands often struggle to understand:

• Why certain posts go viral
• How caption tone impacts engagement
• Which hashtags improve reach
• How to optimize content before publishing

Most analytics tools only display numbers — not actionable insights.

Social Pulse Analyzer converts engagement data into predictive intelligence.

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🔍 Core Features

• 📈 Virality Score Prediction using Machine Learning
• 😊 Sentiment Analysis with interactive visualizations
• 💡 Smart Caption & Hashtag Suggestions
• 🗣️ Audio Summary using gTTS (Text-to-Speech)
• 🌍 Multilingual Support (English & Hindi)
• 🧾 User Dashboard with analysis history

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🧠 How It Works

1. User inputs caption or uploads post data
2. Engagement metrics are calculated
3. ML model predicts virality score
4. Sentiment analysis evaluates tone
5. System generates caption improvements
6. Optional audio summary is created

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🛠 Tech Stack

Backend

• Python (Flask)
• Scikit-Learn
• Pandas
• NumPy

Frontend

• HTML / CSS / JavaScript
• Tailwind CSS
• Bootstrap

AI & NLP

• TextBlob (Sentiment Analysis)
• gTTS (Text-to-Speech)
• Custom ML model for virality prediction

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📈 Business Use Case

For creators managing 100+ posts monthly:

• Identify high-performing content patterns
• Optimize captions before publishing
• Improve organic reach
• Make data-driven content decisions

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🧪 Run Locally

git clone https://github.com/divyant10/SocialPulseAnalyzer.git cd SocialPulseAnalyzer pip install -r requirements.txt python backend/app.py Then open in browser: http://localhost:5000

📂 Project Structure

backend/        # Flask backend & ML logic
frontend/       # UI components
models/         # Trained ML models
data/           # Sample datasets
requirements.txt

🔮 Future Enhancements

• Instagram/Twitter API integration
• SHAP-based model explainability
• Trend detection engine
• SaaS version with authentication
• Cloud storage integration

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👨‍💻 Author

Divyant Mayank
Machine Learning | Data Analytics | Product-Focused Builder

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