This article provides a complete, practical walkthrough of the Mattermost Typeform Automate Triggered 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 Negative Event Feedback Alerts Using n8n, Typeform, AWS Comprehend, and Mattermost  
   
   Meta Description:  
   Learn how to build an automated workflow in n8n that captures event feedback from Typeform, analyzes sentiment using AWS Comprehend, and sends instant negative feedback alerts to a Mattermost channel.  
   
   Keywords:  
   n8n workflow automation, AWS Comprehend sentiment analysis, Typeform feedback automation, Mattermost integration, negative sentiment alert, automated feedback monitoring, AI sentiment detection, no-code automation, event feedback analysis  
   
   Third-party APIs Used:  
   
   - Typeform API  
   - AWS Comprehend API  
   - Mattermost API  
   
   Article:  
   
   Collecting feedback is vital to improving the quality of events, services, or products. However, manually sifting through responses—especially to identify critical or negative sentiment—can be time-consuming and inefficient. Enter automation.
   
   In this article, we explore an intelligent feedback processing workflow built using n8n, an open-source workflow automation tool. This workflow automates the collection, analysis, and distribution of negative feedback from a Typeform survey using AWS Comprehend for sentiment analysis and Mattermost for real-time team notifications.
   
   Let's break down how each component in this workflow contributes to a seamless, efficient feedback monitoring system.
   
   The Automation Workflow Overview  
   
   This n8n workflow performs the following sequence of tasks:
   
   1. Captures new feedback submissions via a Typeform survey.
   2. Analyzes the sentiment of the feedback using Amazon's AWS Comprehend.
   3. Checks if the sentiment is negative.
   4. If so, automatically sends a formatted alert to a specified Mattermost channel.
   5. If the feedback is not negative, the system effectively ignores the input (NoOp).
   
   This setup ensures that only critical feedback is escalated, helping teams respond to issues swiftly without being flooded with less urgent responses.
   
   Let’s take a closer look at each stage in the workflow.
   
   1. Typeform Trigger  
   
   The workflow begins with the Typeform Trigger node, configured to listen for new form responses. It is set to monitor a specific form (with form ID DuJHEGW5), which includes a question like "What did you think about the event?". Whenever someone fills out this form and submits their response, the workflow is activated automatically.
   
   This integration uses the Typeform API via n8n’s built-in Typeform Trigger node and is authenticated through pre-configured credentials.
   
   2. AWS Comprehend - Sentiment Analysis  
   
   Next, the response text from the Typeform submission is fed into AWS Comprehend using the AWS Comprehend node. This cloud-based Natural Language Processing (NLP) service analyzes the tone of the feedback and classifies it as POSITIVE, NEGATIVE, NEUTRAL, or MIXED. It also provides a score indicating the degree of confidence for each sentiment.
   
   For example, a submission like “The event was disorganized and disappointing” would likely be tagged as NEGATIVE with a high confidence score.
   
   This step uses the AWS Comprehend API, relying on previously established AWS credentials in n8n to securely access the cloud-based sentiment service.
   
   3. Conditional Check With IF Node  
   
   The workflow then evaluates the sentiment using an IF node. The condition checks whether the sentiment returned from AWS Comprehend is “NEGATIVE”.
   
   - If the sentiment is negative: The workflow proceeds to notify the team via Mattermost.
   - If the sentiment is not negative: The workflow continues to a NoOp (No Operation) node, essentially ending the process without any further action.
   
   This binary decision gate ensures that the team only gets alerted to feedback that requires immediate attention.
   
   4. Mattermost - Real-Time Alerts  
   
   For feedback classified as negative, the Mattermost node constructs and sends a message to a specific communication channel (ID: h7cxrd1cefr13x689enzyw7xhc).
   
   The message is personalized and includes both the sentiment score and the actual feedback text. A typical alert might look like:
   
   “You got new feedback with a score of 0.82 negative. Here is what it says: The event was disorganized and disappointing.”
   
   This integration uses the Mattermost API, authenticated using stored Mattermost credentials in the n8n node configuration. It allows team members to react immediately, potentially reaching out to the attendee or adjusting future event plans.
   
   Workflow Highlights  
   
   - Real-time Monitoring: The process runs automatically and responds the moment feedback is submitted.
   - Intelligent Filtering: Only feedback with negative sentiment is acted upon, saving time and avoiding alert fatigue.
   - Multi-platform Integration: Seamless connectivity between Typeform, AWS Comprehend, and Mattermost showcases the versatility of n8n.
   - Scalable and Customizable: Easily adapt this template to evaluate other forms of communication like emails, support tickets, or social media comments.
   
   Use Cases Beyond Events  
   
   While this specific setup is geared toward event feedback, it can be adapted for a variety of use cases:
   
   - Customer support satisfaction monitoring
   - Product review filtering
   - Employee pulse survey escalations
   - Continuous performance evaluations
   
   Final Thoughts  
   
   With this n8n workflow, you can transform passive data collection into proactive sentiment monitoring. By leveraging intelligent API integrations, teams are equipped to respond quickly and appropriately to negative feedback, improving both user satisfaction and operational efficiency.
   
   This low-code, easily customizable workflow empowers non-developers and developers alike to build powerful automation chains tailored to real business problems.
   
   If feedback is crucial to your product or service, don’t just collect it—act on it, intelligently and efficiently.
   
   Want to try it yourself? Clone this workflow and connect your own Typeform, AWS Comprehend, and Mattermost accounts to see it in action.
   
   Empower your team with faster feedback loops—because what your users feel matters.
   
   —  
   Written by your AI assistant,  
   Empowering workflows one node at a time.

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.