Research Engineering Intern Assignment

Social Media Analyzer

Introduction

The Social Media Analyzer is a powerful tool designed to analyze social media interactions, focusing on Reddit datasets. The system processes large volumes of data to extract insights, trends, and user behavior patterns using advanced AI models, Graph RAG, and AI-powered analysis.

The project is structured into two main components:

• Client: Frontend interface for visualization and interaction.
• AI Server: Backend processing using NLP, Graph RAG with Neo4j, and AI analysis.

📺 Demo

🎥 Click the badge above to watch Social Media Analyzer in action!

Features

• Chatbot powered by Llama3-8B: Refines user queries and generates responses based on an optimized prompt.
• Graph RAG with Neo4j AuraDB: Extracts insights using network analysis with NetworkX and Neo4j.
• Time Series Data Analysis: Displays user engagement over time.
• Community Distribution Analysis: Identifies the most engaged posts within social communities.
• Topic Trends Detection: Determines trending topics based on the provided query.
• Network Graph Visualization: Generates an interactive visualized graph from Neo4j and provides a summarized analysis.
• AI-powered Analysis:
  • Identifies Main Themes
  • Extracts Key Points
  • Determines Overall Sentiment
  • Highlights Notable Patterns
• Interactive Dashboard: Built with Next.js for an engaging and responsive user experience.

System Architecture

Folder Structure

├── client/          # Frontend application
│   ├── app/         # Application components and pages
│   ├── components/  # Components of social media analyzer
│   ├── utils/       # Backend API connection
│   └── package.json # Frontend dependencies
├── ai-server/       # Backend AI server
│   ├── services/    # Where services code is placed
│   ├── data/        # jsonl and processed_json data
│   ├── scripts/     # Neo4j script for inserting data in the Neo4j AuraDB
│   └── main.py      # Entry point and FastAPI routes
├── images/          # System architecture and UI screenshots
└── README.md        # Project documentation

Installation

Prerequisites

Ensure you have the following installed:

• Node.js > 20 (for client and API server)
• Python 3.11.11 (for AI processing)
• Neo4j (for Graph RAG processing)
• FastAPI (for backend REST API)

Setup

1. Clone the Repository

git clone https://github.com/sameer240704/research-engineering-intern-assignment-sameer-gupta.git

2. Install Dependencies

Client

cd client
npm install

AI Server

cd ../ai-server
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

3. Run the Application

Start AI Server

cd ai-server
uvicorn main:app --host 0.0.0.0 --port 8000

Start Client

cd ../client
npm run dev

Usage

1. Open the web application at http://localhost:3000
2. Enter your query or let the chatbot refine your query.
3. Click on the Analyze button after adding filters.
4. View insights on Time Series Trends, Community Distribution, and Topic Trends.
5. Explore Network Graph relationships between users and topics.
6. Check AI-powered analysis, including themes, key points, sentiment, and patterns.
7. Analyze social media engagement through dynamic charts and reports.

Screenshots

Dashboard View

Graph RAG Visualization

Topic Trends Analysis

Network Graph Summary

AI Analysis

Llama Chatbot

Contributing

Feel free to contribute by opening issues or submitting pull requests!

Author: Sameer Gupta