📌 Reddit Topic Modelling

📖 Overview

This project performs topic modeling on Reddit posts using BERTopic. It retrieves and processes data from Reddit, applies topic modeling, and visualizes key topics discussed within a subreddit. 🚀

🔧 Features

• ✅ Data Retrieval: Extracts posts and comments from Reddit 📥
• ✅ Text Preprocessing: Cleans and prepares text for analysis 🛠️
• ✅ Topic Modelling: Uses BERTopic to identify key discussion topics 🎯
• ✅ Visualization: Generates insightful visual representations of the extracted topics 📊
• ✅ Labelling Docs: Generates a well labelled Json file for your Docs file, segregating each document to its topic (example at the end)

📂 Project Structure

📁 Reddit Project
│-- 📜 README.md
│-- 📜 Data_retrieval.ipynb  # Fetches Reddit data using API
│-- 📜 main.ipynb            # Preprocessing & Topic Modeling
│-- 📁 data                  # Stores raw & processed data
│-- 📁 models                # Trained topic models
│-- 📁 visualizations        # Graphs and topic word clouds

🛠️ Setup & Installation

1️⃣ Clone the Repository

git clone https://github.com/Nikhil-1705/reddit_topic_modelling.git
cd reddit_topic_modelling

2️⃣ Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # Mac/Linux
venv\Scripts\activate     # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

🚀 How to Run the Project

1. Run Data Retrieval Notebook 📥

   jupyter notebook Data_retrieval.ipynb

2. Run Topic Modeling Notebook 🧠

   jupyter notebook main.ipynb

📊 Output Examples

• Word Clouds showing top words in topics 🌥️
• Bar Charts ranking most common topics 📊
• Tables listing top keywords per topic 📜

📝 Contributing

Contributions are welcome! Feel free to fork the repository, create a new branch, and submit a pull request. 🤝

📜 License

This project is licensed under the MIT License. 🔓

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📩 Developed by Nikhil Bhandari |