This article provides a complete, practical walkthrough of the Postgres Googlecloudnaturallanguage Automation Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Social Sentiment Analysis with n8n: A Serverless ETL Pipeline for Twitter Data  
   
   Meta Description:  
   Learn how to build an automated ETL workflow using n8n to extract tweets with the #OnThisDay hashtag, perform sentiment analysis with Google Cloud NLP, store results, and post key insights to Slack.
   
   Keywords:  
   n8n workflow, ETL pipeline with n8n, Twitter sentiment analysis, Google Cloud Natural Language API, Slack integration, MongoDB, Postgres, serverless automation, #OnThisDay tweets, natural language processing, social media analytics, n8n Twitter bot
   
   Third-Party APIs Used:
   
   - Twitter API (via OAuth1)
   - Google Cloud Natural Language API
   - Slack API
   - MongoDB
   - PostgreSQL
   
   Article:
   
   Automating Sentiment Analysis on Twitter Using n8n: A Full ETL Pipeline  
   
   In today's digital age, social media platforms like Twitter provide a goldmine of real-time insights into public opinion and trends. But extracting meaningful signals from the noise requires a blend of automation, data processing, and sentiment analysis. That's where powerful tools like n8n shine.
   
   In this article, we break down how to use n8n — a powerful, fair-code workflow automation tool — to build an end-to-end ETL (Extract, Transform, Load) pipeline that scrapes tweets with the hashtag #OnThisDay, analyzes their sentiment scores using natural language processing, and routes the results through Slack and databases for storage and review.
   
   Let’s explore how each step of this well-orchestrated automation works.
   
   Step 1: Scheduled Twitter Extraction with Cron  
   The workflow begins with an n8n Cron node configured to run daily at 6 AM. This trigger initiates the process by calling the Twitter node, which searches for the latest tweets containing the hashtag #OnThisDay. This could be used for educational, historical, or cultural content aggregation tasks.
   
   In this example, the Twitter node is configured to return a maximum of 3 tweets per run. This is ideal for sample monitoring or lightweight demonstrations, but can be scaled further based on API and system limits.
   
   Step 2: MongoDB Ingestion for Raw Storage  
   Once the tweet data is extracted, it's immediately stored in a MongoDB collection named tweets. Archiving tweets at this step ensures that you maintain a raw, unprocessed dataset that can be useful for future reprocessing or auditing.
   
   MongoDB’s flexible JSON document structure makes it a great choice for initial storage of variable data like social posts.
   
   Step 3: Sentiment Analysis with Google Cloud NLP  
   Each tweet’s text is then sent to the Google Cloud Natural Language node. This API evaluates the sentiment of the tweet content by returning two key metrics:
   
   - Score: A numerical measure from -1 (negative) to 1 (positive)
   - Magnitude: The overall strength or intensity of the sentiment, regardless of polarity
   
   By analyzing the emotion expressed in each tweet, we convert unstructured thoughts into quantifiable sentiment metrics.
   
   Step 4: Data Formatting Using Set Node  
   The next step involves formatting the analyzed text and sentiment data using n8n’s Set node. Here, we extract the sentiment score and magnitude from the Google API response and combine it with the original tweet text pulled from the Twitter node.
   
   This configuration prepares the data in a structured format suitable for relational database ingestion.
   
   Step 5: Structured Data Storage with Postgres  
   The structured data — consisting of tweet text, sentiment score, and magnitude — is then written into a PostgreSQL table named tweets. Postgres serves as a more rigid, query-optimized repository for analytics and reporting. This supports future dashboards, aggregates, or deeper statistical studies of sentiment trends over time.
   
   Step 6: Conditional Slack Notification  
   Post-storing, the workflow introduces a decision gate via the IF node. It evaluates whether the sentiment score exceeds a certain threshold (though the exact threshold value wasn’t defined in this case). If the sentiment is deemed sufficiently strong (e.g., unusually positive or negative), the workflow triggers a customized Slack message.
   
   The Slack node posts directly to a predefined channel (e.g., #tweets) using a templated message:
   
   🐦 NEW TWEET with sentiment score [x] and magnitude [y] ⬇️  
   [original tweet text]
   
   This alert could power real-time monitoring of branded hashtags, campaign reactions, or public event sentiment — giving teams instant visibility.
   
   Otherwise, if the sentiment is not strong enough to merit an alert, the workflow terminates via the NoOp (no-operation) node.
   
   Why This Workflow Matters  
   By chaining together multiple nodes using n8n — including Twitter, MongoDB, Google Cloud NLP, Postgres, and Slack — this workflow demonstrates an elegant data pipeline that’s both powerful and easy to maintain. It showcases the ability to:
   
   - Extract unstructured data from external APIs (Twitter)
   - Store raw and processed data in appropriate storage layers (MongoDB, Postgres)
   - Apply machine learning/NLP tools for human-like text understanding (Google Cloud Natural Language)
   - Automate alerts and feedback loops in team environments (Slack)
   
   Because the workflow is container- and cloud-friendly, it scales well for real-world business automation scenarios — from brand monitoring to market research and more.
   
   Conclusion  
   This end-to-end ETL pipeline using n8n is a robust starting point for automating social media analysis. By marrying data extraction, sentiment analysis, storage, and real-time reporting, it eliminates manual monitoring efforts and turns tweets into actionable insights. Whether you're a data analyst, growth marketer, or automation enthusiast, this workflow can be extended to fit a range of use cases.
   
   Ready to build your own n8n-powered intelligence agent? Clone, customize, and connect your ecosystem — and let automation do the heavy lifting.
   
   🌐 Pro Tip: Try adjusting the tweet limit, sentiment thresholds, or hashtag to fit your content sourcing needs!
   
   —  
   Built entirely using open standards and third-party APIs: Twitter OAuth, Google Cloud NLP, Slack API, MongoDB, and PostgreSQL — all orchestrated through n8n.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.