Social Buzz Heatmap: Automate Sentiment Analysis and Trend Tracking
detail.loadingPreview
This n8n workflow automates the creation of a social buzz heatmap by leveraging Langchain. It captures incoming data via a Webhook, processes it with a Text Splitter and Embeddings, stores it in a Supabase vector store, and uses an AI Agent to analyze sentiment and trends, logging the results to Google Sheets.
🚀Ready to Deploy This Workflow?
About This Workflow
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.
Key Features
- Automated Data Ingestion: Receives social buzz data directly via a Webhook.
- AI-Powered Text Analysis: Utilizes Langchain for intelligent text splitting, embedding generation, and sentiment analysis.
- Scalable Vector Storage: Integrates with Supabase to store and query embeddings efficiently.
- Dynamic Trend Identification: Employs an AI Agent to interpret data and identify key trends.
- Logging and Reporting: Automatically logs analyzed results to a Google Sheet for easy review and historical tracking.
How To Use
- Configure Webhook: Set up your incoming data source to send social buzz information to the n8n Webhook node. Ensure the data format is compatible with the subsequent nodes.
- Set up Langchain Integration: Configure the
Text Splitter,Embeddings(using OpenAI), andVector Store Supabasenodes with your desired settings and API credentials. - Configure Supabase: Ensure your Supabase project is set up and you have an index named
social_buzz_heatmap(or adjust the node parameter accordingly). - Define AI Agent: Configure the
Agentnode with appropriate prompts and tools. Connect theChatnode for language model interaction. - Set up Google Sheets Logging: Configure the
Sheetnode with your Google Sheets API credentials and specify theSHEET_IDandLogsheet name where you want to store the results. - Activate and Monitor: Run the workflow and send test data to the Webhook to observe the automated analysis and logging process.
Apps Used
Workflow JSON
{
"id": "4ca5f46d-e20f-4f32-a6e6-b155bff4823c",
"name": "Social Buzz Heatmap: Automate Sentiment Analysis and Trend Tracking",
"nodes": 0,
"category": "AI & Machine Learning",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 4ca5f46d-e20f...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
Statistics
Verification Info
Related Workflows
Discover more workflows you might like
Chat with Local LLMs via Ollama
Integrate and chat with your local Large Language Models using Ollama and n8n.
Telegram AI Langchain Bot with DALL-E 3 Image Generation
An n8n workflow that acts as a Telegram bot, powered by Langchain, for AI chat interactions and image generation using DALL-E 3.
Visa Requirement Checker
A workflow to check visa requirements based on user input, leveraging Langchain, Cohere embeddings, Weaviate vector store, and Anthropic LLM.