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: A Smart ETL Workflow with n8n, Twitter, and Google Cloud NLP
   
   Meta Description:
   Discover how to build a fully automated ETL pipeline using n8n that fetches #OnThisDay tweets, analyzes sentiment with Google Cloud Natural Language API, stores the results in PostgreSQL and MongoDB, and alerts your Slack team.
   
   Keywords:
   ETL automation, n8n workflow, Twitter API, sentiment analysis, Google Cloud Natural Language API, Slack integration, social media analytics, MongoDB, PostgreSQL, natural language processing
   
   Third-party APIs Used:
   - Twitter API (via n8n)
   - Google Cloud Natural Language API
   - Slack API
   - MongoDB (via n8n)
   - PostgreSQL (via n8n)
   
   Article:
   
   Automating Social Sentiment Analysis: A Smart ETL Workflow with n8n, Twitter, and Google Cloud NLP
   
   In today's data-driven world, keeping track of public sentiment can give businesses and individuals a strategic edge. Whether you're a brand strategist, a historian, or a social media analyst, monitoring sentiments behind trending topics like #OnThisDay can reveal valuable patterns. But manually collecting and analyzing this kind of data? Time-consuming, inconsistent, and error-prone.
   
   This is where low-code automation tools like n8n come in. n8n ("nodemation") is an open-source workflow automation tool that connects different services, APIs, and databases, enabling you to create robust pipelines with minimal code. Today, we’ll walk you through a real-world ETL (Extract, Transform, Load) workflow built with n8n that:
   
   - Pulls tweets using the hashtag #OnThisDay
   - Stores the raw tweet data in MongoDB
   - Analyzes tweet sentiment using Google Cloud Natural Language API
   - Stores enriched data in PostgreSQL
   - Sends a Slack alert if the sentiment score crosses a specific threshold
   
   Let’s break down how this works step by step.
   
   Step 1: Set a Daily Trigger with Cron
   
   The pipeline kicks off with a Cron node, configured to run every day at 6 AM. This ensures we capture topical #OnThisDay tweets as they emerge. The cron-based schedule is ideal for daily reporting or trend-tracking workflows.
   
   Step 2: Extract Tweets Using the Twitter API
   
   Next, the Twitter node is used to search for the top three tweets containing the #OnThisDay hashtag. By pulling only a limited number of tweets (in this case, three), the workflow remains lightweight and quick to execute. The Twitter API is connected using OAuth for secure access.
   
   Step 3: Load Raw Tweets into MongoDB
   
   Before any analysis, the raw tweet text is inserted into a MongoDB collection called “tweets”. Storing raw data at this stage gives you a backup for future re-analysis or training machine learning models. MongoDB serves as a flexible document store for unstructured or semi-structured data like tweets.
   
   Step 4: Transform with Sentiment Analysis
   
   Once the tweets are stored, each one is fed into the Google Cloud Natural Language API, which evaluates the sentiment of the text. The API returns two key metrics:
   
   - Sentiment Score: Ranges from -1 (negative) to 1 (positive)
   - Magnitude: Represents the overall strength of sentiment, regardless of polarity
   
   These values quantify the emotion expressed in the tweet, giving structure to otherwise subjective data.
   
   Step 5: Reformat and Prepare Data for SQL Storage
   
   The Set node then reformats this processed data. It organizes the fields — tweet text, score, and magnitude — for structured database storage. This normalization is crucial before persisting data in a relational database like PostgreSQL.
   
   Step 6: Persist Sentiment-Enhanced Tweets in PostgreSQL
   
   Using the PostgreSQL node, enriched tweets are inserted into a table named “tweets”. This table will have three columns: text, score, and magnitude. PostgreSQL gives you the power to query, filter, and aggregate data for reports or dashboards.
   
   Step 7: Conditional Logic for Slack Notification
   
   The workflow continues with an “IF” node to check if the sentiment score is above a certain threshold. While the threshold isn't specified in the configuration, we can assume it filters for especially positive tweets — for example, those scoring above 0.5.
   
   Step 8: Let Slack Know!
   
   If a tweet passes the positive sentiment test, a message gets sent to a Slack channel named "tweets". The Slack message includes the tweet content along with its sentiment score and magnitude, formatted succinctly:
   
   🐦 NEW TWEET with sentiment score 0.8 and magnitude 1.2 ⬇️  
   "This day in history: man walks on the moon. 🚀"
   
   This kind of alert is useful for community managers, historians, or marketing teams who wish to highlight meaningful content.
   
   If the tweet doesn't meet the sentiment condition, it flows to a NoOp (No Operation) node, effectively ending its journey through the pipeline.
   
   Why This Workflow is Powerful
   
   - Fully Automated: Runs daily with zero manual input
   - Scalable: Add filters, languages, or more NLP tools as needed
   - Insightful: Stores and shares only meaningful, sentiment-rich content
   - Modular: Can be expanded to include dashboards, reports, or more ML APIs
   
   Conclusion
   
   This n8n workflow elegantly demonstrates how low-code automation can bridge multiple APIs and data systems to produce measurable insights. From fetching social media content to performing real-time sentiment analysis, everything is streamlined in a way that’s both human- and machine-friendly.
   
   With modular design and open integrations, n8n empowers you to customize and expand this ETL pipeline to suit your exact needs — whether that's tracking other hashtags, applying language translation, or plugging into visualization tools like Grafana or Power BI.
   
   Ready to automate your own data intelligence? This workflow is a great place to start.
   
   🛠️ Happy automating!

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.