Back to Catalog

Track regional sentiment from social media with Bright Data & OpenAI

Yaron BeenYaron Been
417 views
2/3/2026
Official Page

This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

This workflow automatically tracks regional sentiment across social media and news outlets, giving you a real-time pulse on how people in a specific area feel about your brand or topic.

Overview

The automation queries Twitter, Reddit, and major news APIs filtered by geolocation. Bright Data handles location-specific scraping where APIs are limited. OpenAI performs sentiment and keyword extraction, aggregating scores into a daily report stored in Google Sheets and visualized in Data Studio.

Tools Used

  • n8n – Coordinates all steps
  • Bright Data – Collects geo-targeted data beyond API limits
  • OpenAI – Runs sentiment analysis and topic modeling
  • Google Sheets – Houses cleaned sentiment metrics
  • Data Studio / Looker – Optional dashboard for visualization

How to Install

  1. Import the Workflow into n8n with the provided .json.
  2. Configure Bright Data credentials.
  3. Set Up OpenAI API key.
  4. Connect Google Sheets and create a destination spreadsheet.
  5. Customize Regions & Keywords in the Start node.

Use Cases

  • Brand Monitoring: Measure public opinion in target markets.
  • Political Campaigns: Gauge voter sentiment by district.
  • Market Entry: Understand regional attitudes before launching.
  • Crisis Management: Detect negative spikes early.

Connect with Me

  • Website: https://www.nofluff.online
  • YouTube: https://www.youtube.com/@YaronBeen/videos
  • LinkedIn: https://www.linkedin.com/in/yaronbeen/
  • Get Bright Data: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission)

#n8n #automation #sentimentanalysis #geolocation #brightdata #openai #sociallistening #n8nworkflow #nocode #brandmonitoring

AI-Powered Trello Task Creation

This n8n workflow demonstrates how to leverage AI to process information and create structured tasks in Trello, triggered manually. It showcases the integration of LangChain AI agents, OpenAI chat models, and structured output parsing to automate task management.

What it does

This workflow, when manually triggered, performs the following steps:

  1. Manual Trigger: Initiates the workflow upon a user's manual execution.
  2. AI Agent (LangChain): Utilizes an AI Agent to process an input. This agent is configured with an OpenAI Chat Model and an Auto-fixing Structured Output Parser to understand and structure the AI's response.
  3. OpenAI Chat Model: Communicates with the OpenAI API to generate a response based on the input provided to the AI Agent.
  4. Auto-fixing Output Parser: Attempts to parse the AI's response into a structured format, automatically correcting minor errors to ensure valid output.
  5. Structured Output Parser: Enforces a predefined JSON schema on the AI's output, ensuring that the data is consistently formatted for subsequent nodes.
  6. Edit Fields (Set): (This node is present but not connected in the provided JSON, suggesting it might be a placeholder or intended for future use to manipulate the AI's output before Trello).
  7. Trello: (This node is present but not connected in the provided JSON, suggesting it would be used to create cards or tasks in Trello based on the structured output from the AI).

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: An API key for OpenAI to use the OpenAI Chat Model node. This should be configured as an n8n credential.
  • Trello Account: A Trello account to create tasks. (Requires Trello credentials if the Trello node is connected and used).

Setup/Usage

  1. Import the workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API key as a credential in n8n and select it in the OpenAI Chat Model node.
    • (Optional) If you plan to connect and use the Trello node, set up your Trello API Key and Token as a credential in n8n.
  3. Connect Nodes (if applicable): The Edit Fields and Trello nodes are currently disconnected. To make the workflow functional for Trello task creation, you would need to:
    • Connect the output of the Structured Output Parser to the input of the Edit Fields node (if data manipulation is needed).
    • Connect the output of the Structured Output Parser (or Edit Fields) to the input of the Trello node.
    • Configure the Trello node to create cards, specifying the board, list, and card details using expressions from the AI's structured output.
  4. Activate the Workflow: Once configured, activate the workflow.
  5. Execute Manually: Click "Execute Workflow" in the n8n editor to run the workflow. The AI Agent will process its configured prompt and generate structured output.

Related Templates

AI multi-agent executive team for entrepreneurs with Gemini, Perplexity and WhatsApp

This workflow is an AI-powered multi-agent system built for startup founders and small business owners who want to automate decision-making, accountability, research, and communication, all through WhatsApp. The “virtual executive team,” is designed to help small teams to work smarter. This workflow sends you market analysis, market and sales tips, It can also monitor what your competitors are doing using perplexity (Research agent) and help you stay a head, or make better decisions. And when you feeling stuck with your start-up accountability director is creative enough to break the barrier 🎯 Core Features 🧑‍💼 1. President (Super Agent) Acts as the main controller that coordinates all sub-agents. Routes messages, assigns tasks, and ensures workflow synchronization between the AI Directors. 📊 2. Sales & Marketing Director Uses SerpAPI to search for market opportunities, leads, and trends. Suggests marketing campaigns, keywords, or outreach ideas. Can analyze current engagement metrics to adjust content strategy. 🕵️‍♀️ 3. Business Research Director Powered by Perplexity AI for competitive and market analysis. Monitors competitor moves, social media engagement, and product changes. Provides concise insights to help the founder adapt and stay ahead. ⏰ 4. Accountability Director Keeps the founder and executive team on track. Sends motivational nudges, task reminders, and progress reports. Promotes consistency and discipline — key traits for early-stage success. 🗓️ 5. Executive Secretary Handles scheduling, email drafting, and reminders. Connects with Google Calendar, Gmail, and Sheets through OAuth. Automates follow-ups, meeting summaries, and notifications directly via WhatsApp. 💬 WhatsApp as the Main Interface Interact naturally with your AI team through WhatsApp Business API. All responses, updates, and summaries are delivered to your chat. Ideal for founders who want to manage operations on the go. ⚙️ How It Works Trigger: The workflow starts from a WhatsApp Trigger node (via Meta Developer Account). Routing: The President agent analyzes the incoming message and determines which Director should handle it. Processing: Marketing or sales queries go to the Sales & Marketing Director. Research questions are handled by the Business Research Director. Accountability tasks are assigned to the Accountability Director. Scheduling or communication requests are managed by the Secretary. Collaboration: Each sub-agent returns results to the President, who summarizes and sends the reply back via WhatsApp. Memory: Context is maintained between sessions, ensuring personalized and coherent communication. 🧩 Integrations Required Gemini API – for general intelligence and task reasoning Supabase- for RAG and postgres persistent memory Perplexity API – for business and competitor analysis SerpAPI – for market research and opportunity scouting Google OAuth – to connect Sheets, Calendar, and Gmail WhatsApp Business API – for message triggers and responses 🚀 Benefits Acts like a team of tireless employees available 24/7. Saves time by automating research, reminders, and communication. Enhances accountability and strategy consistency for founders. Keeps operations centralized in a simple WhatsApp interface. 🧰 Setup Steps Create API credentials for: WhatsApp (via Meta Developer Account) Gemini, Perplexity, and SerpAPI Google OAuth (Sheets, Calendar, Gmail) Create a supabase account at supabase Add the credentials in the corresponding n8n nodes. Customize the system prompts for each Director based on your startup’s needs. Activate and start interacting with your virtual executive team on WhatsApp. Use Case You are a small organisation or start-up that can not afford hiring; marketing department, research department and secretar office, then this workflow is for you 💡 Need Customization? Want to tailor it for your startup or integrate with CRM tools like Notion or HubSpot? You can easily extend the workflow or contact the creator for personalized support. Consider adjusting the system prompt to suite your business

ShadrackBy Shadrack
331

Document RAG & chat agent: Google Drive to Qdrant with Mistral OCR

Knowledge RAG & AI Chat Agent: Google Drive to Qdrant Description This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it. It’s composed of two distinct flows: An ingestion pipeline to process documents. A live chat agent that uses RAG (Retrieval-Augmented Generation) and optional web search to answer user questions. This system fully automates the creation of a “Chat with your docs” solution and enhances it with external web-searching capabilities. --- Quick Implementation Steps Import the workflow JSON into your n8n instance. Set up credentials for Google Drive, Mistral AI, OpenAI, and Qdrant. Open the Web Search node and add your Tavily AI API key to the Authorization header. In the Google Drive (List Files) node, set the Folder ID you want to ingest. Run the workflow manually once to populate your Qdrant database (Flow 1). Activate the workflow to enable the chat trigger (Flow 2). Copy the public webhook URL from the When chat message received node and open it in a new tab to start chatting. --- What It Does The workflow is divided into two primary functions: Knowledge Base Ingestion (Manual Trigger) This flow populates your vector database. Scans Google Drive: Lists all files from a specified folder. Processes Files Individually: Downloads each file. Extracts Text via OCR: Uses Mistral AI OCR API for text extraction from PDFs, images, etc. Generates Smart Metadata: A Mistral LLM assigns metadata like documenttype, project, and assignedto. Chunks & Embeds: Text is cleaned, chunked, and embedded via OpenAI’s text-embedding-3-small model. Stores in Qdrant: Text chunks, embeddings, and metadata are stored in a Qdrant collection (docaiauto). AI Chat Agent (Chat Trigger) This flow powers the conversational interface. Handles User Queries: Triggered when a user sends a chat message. Internal RAG Retrieval: Searches Qdrant Vector Store first for answers. Web Search Fallback: If unavailable internally, the agent offers to perform a Tavily AI web search. Contextual Responses: Combines internal and external info for comprehensive answers. --- Who's It For Ideal for: Teams building internal AI knowledge bases from Google Drive. Developers creating AI-powered support, research, or onboarding bots. Organizations implementing RAG pipelines. Anyone making unstructured Google Drive documents searchable via chat. --- Requirements n8n instance (self-hosted or cloud). Google Drive Credentials (to list and download files). Mistral AI API Key (for OCR & metadata extraction). OpenAI API Key (for embeddings and chat LLM). Qdrant instance (cloud or self-hosted). Tavily AI API Key (for web search). --- How It Works The workflow runs two independent flows in parallel: Flow 1: Ingestion Pipeline (Manual Trigger) List Files: Fetch files from Google Drive using the Folder ID. Loop & Download: Each file is processed one by one. OCR Processing: Upload file to Mistral Retrieve signed URL Extract text using Mistral DOC OCR Metadata Extraction: Analyze text using a Mistral LLM. Text Cleaning & Chunking: Split into 1000-character chunks. Embeddings Creation: Use OpenAI embeddings. Vector Insertion: Push chunks + metadata into Qdrant. Flow 2: AI Chat Agent (Chat Trigger) Chat Trigger: Starts when a chat message is received. AI Agent: Uses OpenAI + Simple Memory to process context. RAG Retrieval: Queries Qdrant for related data. Decision Logic: Found → Form answer. Not found → Ask if user wants web search. Web Search: Performs Tavily web lookup. Final Response: Synthesizes internal + external info. --- How To Set Up Import the Workflow Upload the provided JSON into your n8n instance. Configure Credentials Create and assign: Google Drive → Google Drive nodes Mistral AI → Upload, Signed URL, DOC OCR, Cloud Chat Model OpenAI → Embeddings + Chat Model nodes Qdrant → Vector Store nodes Add Tavily API Key Open Web Search node → Parameters → Headers Add your key under Authorization (e.g., tvly-xxxx). Node Configuration Google Drive (List Files): Set Folder ID. Qdrant Nodes: Ensure same collection name (docaiauto). Run Ingestion (Flow 1) Click Test workflow to populate Qdrant with your Drive documents. Activate Chat (Flow 2) Toggle the workflow ON to enable real-time chat. Test Open the webhook URL and start chatting! --- How To Customize Change LLMs: Swap models in OpenAI or Mistral nodes (e.g., GPT-4o, Claude 3). Modify Prompts: Edit the system message in ai chat agent to alter tone or logic. Chunking Strategy: Adjust chunkSize and chunkOverlap in the Code node. Different Sources: Replace Google Drive with AWS S3, Local Folder, etc. Automate Updates: Add a Cron node for scheduled ingestion. Validation: Add post-processing steps after metadata extraction. Expand Tools: Add more functional nodes like Google Calendar or Calculator. --- Use Case Examples Internal HR Bot: Answer HR-related queries from stored policy docs. Tech Support Assistant: Retrieve troubleshooting steps for products. Research Assistant: Summarize and compare market reports. Project Management Bot: Query document ownership or project status. --- Troubleshooting Guide | Issue | Possible Solution | |------------|------------------------| | Chat agent doesn’t respond | Check OpenAI API key and model availability (e.g., gpt-4.1-mini). | | Known documents not found | Ensure ingestion flow ran and both Qdrant nodes use same collection name. | | OCR node fails | Verify Mistral API key and input file integrity. | | Web search not triggered | Re-check Tavily API key in Web Search node headers. | | Incorrect metadata | Tune Information Extractor prompt or use a stronger Mistral model. | --- Need Help or More Workflows? Want to customize this workflow for your business or integrate it with your existing tools? Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements. We can help you set it up for free — from connecting credentials to deploying it live. Contact: shilpa.raju@digitalbiz.tech Website: https://www.digitalbiz.tech LinkedIn: https://www.linkedin.com/company/digital-biz-tech/ You can also DM us on LinkedIn for any help. ---

DIGITAL BIZ TECHBy DIGITAL BIZ TECH
1409

Automated YouTube video uploads with 12h interval scheduling in JST

This workflow automates a batch upload of multiple videos to YouTube, spacing each upload 12 hours apart in Japan Standard Time (UTC+9) and automatically adding them to a playlist. ⚙️ Workflow Logic Manual Trigger — Starts the workflow manually. List Video Files — Uses a shell command to find all .mp4 files under the specified directory (/opt/downloads/单词卡/A1-A2). Sort and Generate Items — Sorts videos by day number (dayXX) extracted from filenames and assigns a sequential order value. Calculate Publish Schedule (+12h Interval) — Computes the next rounded JST hour plus a configurable buffer (default 30 min). Staggers each video’s scheduled time by order × 12 hours. Converts JST back to UTC for YouTube’s publishAt field. Split in Batches (1 per video) — Iterates over each video item. Read Video File — Loads the corresponding video from disk. Upload to YouTube (Scheduled) — Uploads the video privately with the computed publishAtUtc. Add to Playlist — Adds the newly uploaded video to the target playlist. 🕒 Highlights Timezone-safe: Pure UTC ↔ JST conversion avoids double-offset errors. Sequential scheduling: Ensures each upload is 12 hours apart to prevent clustering. Customizable: Change SPANHOURS, BUFFERMIN, or directory paths easily. Retry-ready: Each upload and playlist step has retry logic to handle transient errors. 💡 Typical Use Cases Multi-part educational video series (e.g., A1–A2 English learning). Regular content release cadence without manual scheduling. Automated YouTube publishing pipelines for pre-produced content. --- Author: Zane Category: Automation / YouTube / Scheduler Timezone: JST (UTC+09:00)

ZaneBy Zane
226