hype-o-thesis 🚀

A Web Stack Powered by Machine Learning and NLP to Track & Predict Social Media Trends

🪼 Project Members:

1. Akshat Chhatriwala
2. Ishita Akolkar
3. Dhruv Joshi
4. Parth Hindiya

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Overview

hype-o-thesis is a full-stack web application that leverages Machine Learning (ML) and Natural Language Processing (NLP) to monitor real-time social media conversations and predict emerging trends. The project analyzes data from platforms like Twitter and Reddit to provide insights into what’s gaining momentum online — from memes and music to movements and breaking news.

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📊 Key Features

• 1. Real-Time Data Scraping from popular social media platforms.
• 2. NLP-based Sentiment & Trend Analysis using transformer models.
• 3. Trend Prediction Engine powered by time series forecasting & ML classifiers.
• 4. Interactive Web Dashboard for visualizing trending keywords, sentiment distribution, and predictions.
• 5. Scalable Web Stack with modular architecture.

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🛠️ Tech Stack

Layer	Technologies Used
Frontend	React.js, Chart.js, Tailwind CSS
Backend	Node.js, Express.js
ML/NLP	Python (scikit-learn, pandas, NLTK, HuggingFace Transformers)
Database	MongoDB
APIs	Twitter API, Reddit API

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🚧 How It Works

1. Data Ingestion: Fetches social media posts using APIs.
2. Preprocessing: Cleans and filters posts using NLP techniques.
3. Analysis: Applies models for sentiment analysis and keyword extraction.
4. Prediction: Uses historical data to forecast trending topics.
5. Visualization: Displays data through an intuitive dashboard.

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Prerequisites

• Node.js and npm
• Python 3.8+
• MongoDB (local or Atlas)
• Twitter & Reddit API keys