CineLenz 🎬
Social Media Movie Analytics Platform — Detect threads, sentiment, and trends in real time
🚀 Overview
CineLenz is a full-stack web application that analyzes social media discussions related to movies and cinematic events. It collects posts from platforms like X (Twitter) and Reddit, groups them into conversation threads, performs sentiment analysis, and visualizes how audience buzz evolves over time.
This project demonstrates real-time data processing, NLP-based clustering, and interactive data visualization.
✨ Key Features
Thread Detection — Groups related posts into conversation clusters using NLP embeddings
Sentiment Analysis — Classifies audience tone as positive, negative, or neutral using multilingual models
Trend Tracking — Identifies trending movies and topics over different time ranges
Timeline Visualization — Displays how discussion volume changes over time
Search Any Movie — Explore live or recent social media threads for any movie
🧩 Tech Stack Frontend
React.js
Tailwind CSS
Chart.js / Recharts
Backend
FastAPI (Python)
REST APIs for data processing
NLP & Data Processing
Sentence Transformers (thread clustering)
HuggingFace multilingual sentiment models
snscrape (X/Twitter scraping)
PRAW (Reddit API)
Deployment
Vercel (Frontend)
⚙️ Architecture Frontend → FastAPI Backend → Social Media Data → NLP Processing → JSON Response → Visual Charts
🛠️ Getting Started 🔹 Clone Repository git clone https://github.com/adithyancp123/cinelenzV2.git cd cinelenzV2
🔹 Frontend Setup npm install npm run dev
🔹 Backend Setup cd src/backend pip install -r requirements.txt uvicorn app:app --reload
📁 Folder Structure app/ → React pages components/ → UI components hooks/ → Custom React hooks lib/ → Utilities src/backend/ → FastAPI backend styles/ → Global styles tests/ → Test cases docs/ → Documentation deployment/ → Deployment configs
🌐 Live Demo
📸 Screenshots
screenshots/home.png screenshots/chart.png screenshots/threads.png
💡 Why CineLenz is Unique
Unlike basic sentiment analysis projects, CineLenz performs:
Thread-level clustering of discussions
Real-time trend evolution tracking
Multi-platform social media data integration
Visual analytics for audience behavior
📌 What This Project Demonstrates
Full-stack application development
REST API design using FastAPI
NLP-based clustering and sentiment analysis
Data visualization and trend analytics
Real-world data handling from social platforms
📄 License
MIT License
👨💻 Author
Adithyan C P BTech Computer Science Student GitHub: https://github.com/adithyancp123