Quantify

Analyze and visualize engagement metrics for social media posts using advanced analytics and data visualization techniques.

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Table of Contents

• Project Overview
• Features
• Tech Stack
• Dataset
• Installation
• Usage
• Screenshots
• Contributing
• License
• Acknowledgments

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Project Overview

Quantify provides actionable insights into social media engagement data. This project utilizes a mock dataset of social media posts, allowing you to:

• Analyze post performance.
• Understand engagement patterns across platforms.
• Extract valuable insights for optimizing social media strategies.

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Features

Engagement Metrics

• Analyze likes, comments, shares, saves, and views for each post.

Sentiment Analysis

• Evaluate post comments' sentiment (positive, neutral, negative).

User Demographics

• Understand engagement patterns across age groups, genders, and locations.

Platform Insights

• Compare performance across Instagram, Facebook, and Twitter.

Hashtag Effectiveness

• Discover the impact of hashtags on engagement rates.

Temporal Trends

• Identify peak engagement times and optimize posting schedules.

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Tech Stack

• Frontend: React.js (TypeScript), Vite, TailwindCSS
• Backend: Node.js, Express.js
• Database: DataStax Astra DB
• AI Analytics: Langflow with GPT Integration
• Visualization: Chart.js, Recharts
• File Parsing: Papa Parse

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Dataset

The mock dataset includes:

• Post Details: Type, content length, hashtags.
• Engagement Metrics: Likes, comments, shares, saves.
• User Info: Age, gender, location.
• Sentiment Analysis: Positive, neutral, negative.
• Platform and Time Trends: Platform-specific and peak times.

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Installation

1. Clone the repository:

   git clone https://github.com/your-username/quantify.git
   cd Quantify

2. Install dependencies:

   npm install

3. Set up the database:

   • Create an account on DataStax Astra DB.
   • Obtain credentials and update the .env file.

4. Start the development server:

   npm start

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Usage

1. Upload a CSV dataset through the app's interface.
2. Explore insights with:
   • Visualized metrics and trends.
   • Sentiment and demographic analysis.
   • Comparison across platforms.

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature-name"

4. Push and open a pull request.

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License

This project is licensed under the MIT License.

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Acknowledgments

Special thanks to:

• Langflow for AI capabilities.
• DataStax Astra DB for database support.
• Open-source libraries and the developer community.