Social Buzz Heatmap: Automate Sentiment Analysis and Trend Tracking

Overview

This n8n workflow, named "Social Buzz Heatmap," is designed to automate the process of tracking and visualizing social media buzz around specific topics. It ingeniously combines data ingestion via a Webhook with advanced AI capabilities from Langchain for text processing, embedding, vector storage, and sentiment analysis. The workflow solves the problem of manually monitoring social media by providing an automated way to capture, analyze, and log sentiment and trending topics. This allows for quick identification of public opinion shifts and emerging trends.

Key components include:

• Webhook: To receive incoming data about social buzz.
• Text Splitter: To break down large text inputs into manageable chunks for AI processing.
• Embeddings: To convert text into numerical representations for AI analysis.
• Supabase Vector Store (Insert & Query): To store and efficiently retrieve embeddings for analysis.
• Langchain Agent: The core AI component that queries the vector store, interacts with a language model, and leverages tools (like memory and other vector stores) to generate insights.
• Google Sheets: To log the analyzed results, creating a traceable record of social buzz trends.