This article provides a complete, practical walkthrough of the Strapi Webhook Automate Webhook 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:  
   Intelligent Content Curation with n8n: An Automated Workflow for Tweet Sentiment Analysis and CMS Integration
   
   Meta Description:  
   Discover how to build a powerful content curation pipeline using n8n to fetch recent tweets, analyze their sentiment with Google Cloud Natural Language, and auto-publish relevant content to Strapi CMS.
   
   Keywords:  
   n8n workflow, sentiment analysis, Google Cloud NLP, Strapi CMS, Twitter automation, content curation, Twitter API, webhook automation, low-code automation, tweet monitoring
   
   Third-Party APIs Used:
   
   - Twitter API (@MutedJam)
   - Google Cloud Natural Language API
   - Strapi API (Strapi Demo)
   
   Article:
   
   Automating Tweet Sentiment Analysis and Content Curation with n8n
   
   In today’s fast-paced digital world, content marketers and social media managers are constantly on the hunt for fresh, relevant user-generated content. Monitoring Twitter manually for quality mentions of your brand or tech stack can be overwhelming. That’s where automation frameworks like n8n make life easier.
   
   This article walks you through an advanced no-code/low-code n8n workflow that automatically fetches tweets mentioning “strapi” or “n8n.io,” analyzes their sentiment using Google Cloud Natural Language, filters out retweets or outdated posts, and stores the most positive content in a Strapi CMS backend. Additionally, it allows for manual submissions through a webhook-enabled form, ensuring that only highly relevant, sentiment-approved content makes it through for publishing.
   
   Let’s break down how each piece of this automation puzzle fits together.
   
   ⏱ 1. Triggering the Workflow: Scheduled and Manual Inputs
   
   The workflow supports two inputs:
   
   - Every 30 Minutes: An interval node triggers the Twitter search module every 30 minutes. It fetches the most recent tweets mentioning “strapi” or “n8n.io” in English using the Twitter API. This ensures content is always fresh.
     
   - Webhook: Simultaneously, the workflow listens for POST requests through a webhook. This allows you or your team to submit content manually via a form or external service. Each submission is subsequently checked for quality as well.
   
   🧹 2. Cleaning and Simplifying the Data
   
   For both tweets and form submissions, n8n “Set” nodes are used to extract and format the data of interest:
   
   - Content (text of the tweet or form input)
   - Author (Twitter handle or name)
   - Created (timestamp)
   - URL (link back to original tweet)
   
   Tweets have their URLs stripped out to focus purely on the message, while form submissions receive a timestamp upon submission.
   
   🚫 3. Early Filtering: Remove the Low-Value Content
   
   Before further processing, the workflow intelligently removes:
   
   - Retweets: Tweets starting with “RT @” are filtered out.
   - Old Tweets: Any tweet older than 30 minutes is discarded.
   
   This ensures only timely and unique content is analyzed.
   
   🧠 4. Analyzing Sentiment with Google Cloud Natural Language
   
   The core of the workflow involves sentiment analysis using the Google Cloud Natural Language API.
   
   - Each tweet or form submission text is sent to the NLP API.
   - The sentiment score is returned (ranging from -1 to +1).
   - A decision is made: Is the sentiment strong enough to be worth featuring?
   
   Thresholds differ slightly to accommodate the nature of input:
   - Tweets require a score higher than 0.3 to be considered positive.
   - Form submissions, due to their direct intent, only need to cross 0.4.
   
   🗃 5. Storing High-Quality Content in Strapi CMS
   
   If the sentiment meets the positivity threshold, the content is published to Strapi.
   
   - The “Strapi” node uses authenticated API access to create a new “post.”
   - Only fields that passed earlier data cleanup and sentiment checks are stored: Content, Author, Created, and URL.
   
   This integration allows your CMS to be populated with valuable user content without manual vetting—saving hours of time weekly.
   
   🔁 6. Scalable and Modular Design
   
   Thanks to n8n's visual interface and modular architecture, this workflow is easily scalable:
   
   - Add more keywords to the Twitter search.
   - Include additional languages.
   - Insert translation nodes for multilingual content.
   - Expand the CMS to multiple platforms (e.g., Ghost, WordPress).
   
   ⚙️ Technical Stack Overview
   
   Here’s a quick summary of the third-party tools and services integrated into this workflow:
   
   - Twitter API: To search for and fetch tweets mentioning keywords of interest.
   - Google Cloud Natural Language API: To perform sentiment analysis on textual content.
   - Strapi API: To store curated content in a CMS for further publishing or internal use.
   
   ✨ Final Thoughts
   
   This n8n workflow offers a highly strategic automation tactic for marketers, social teams, and editors. By combining real-time data from Twitter with powerful sentiment analysis and an open-source CMS, it eliminates noise and highlights positive brand mentions without lifting a finger.
   
   Whether you're managing community feedback or building a testimonials page, this automation ensures you’re leveraging the best content your audience has to offer—at scale, and powered by AI.
   
   Try customizing it for your use case, and take your content pipeline to the next level!

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.