Sentiment Analysis Workflow using Webhook, Hugging Face, Google Sheets & Jira

This workflow automatically analyzes incoming text feedback, classifies it into Positive, Neutral or Negative using a Hugging Face sentiment model, stores results in Google Sheets and creates Jira tickets for negative feedback.

Quick Steps to Get Started

1. Import the workflow into your n8n account
2. Set up Webhook endpoint (/sentiment-input)
3. Add Hugging Face API token in HTTP Request node
4. Configure Google Sheets (3 tabs: Positive, Neutral, Negative)
5. Connect Jira credentials
6. Activate the workflow
7. Send POST request with text data

What It Does

This workflow automates sentiment analysis of incoming text data using a machine learning model hosted on Hugging Face. It receives multiple text inputs via a webhook, processes each input individually and evaluates sentiment scores returned by the model.

The workflow intelligently determines whether the sentiment is Positive, Neutral or Negative based on the highest score and a confidence threshold. It ensures more reliable classification by applying a score validation logic.

Once classified, the workflow routes the data accordingly. Each sentiment category is stored in a separate Google Sheets tab, making it easy to track and analyze feedback trends. Additionally, any negative feedback automatically triggers the creation of a Jira ticket, enabling quick issue resolution.

Who It's For

• Businesses collecting customer feedback
• Product teams monitoring user sentiment
• Customer support teams
• SaaS companies handling reviews or complaints
• Developers building AI-powered automation workflows

Requirements

To use this workflow, you need:

• n8n account (self-hosted or cloud)
• Hugging Face API access token
• Google Sheets account with service account credentials
• Jira Software Cloud account
• Basic understanding of n8n workflows and nodes

How It Works & Setup Guide

Step 1: Import Workflow

• Import the JSON file into n8n

Step 2: Configure Webhook

• Node: Receive Feedback
• Method: POST
• Endpoint: /sentiment-input
• Input format:

{
  "data": [
    { "text": "I love this" },
    { "text": "This is okay" },
    { "text": "Worst experience ever" }
  ]
}

Step 3: Split Input Data

• Node: Split Text Items
• Splits array into individual items for processing

Step 4: Rate Limiting

• Node: Rate Limit Control
• Prevents API overload (optional delay control)

Step 5: Preserve Input

• Node: Preserve Input Text
• Keeps original text intact for later use

Step 6: Sentiment Analysis

• Node: Get Sentiment Scores
• Add Hugging Face API token in headers:
  • Authorization: Bearer YOUR_API_KEY
• Model used:
  cardiffnlp/twitter-roberta-base-sentiment

Step 7: Merge Data

• Node: Merge Text & Scores
• Combines model output with original input

Step 8: Compute Sentiment

• Node: Compute Sentiment
• Logic:
  • Highest score determines sentiment
  • Confidence threshold: > 0.9
  • Otherwise defaults to Neutral

Step 9: Route Data

• Node: Route by Sentiment
• Routes into:
  • Positive
  • Neutral
  • Negative

Step 10: Store Results

• Nodes:
  • Store Positive Feedback
  • Store Neutral Feedback
  • Store Negative Feedback
• Append data into respective Google Sheets tabs

Step 11: Create Jira Ticket

• Node: Create Jira Ticket
• Triggered only for Negative sentiment
• Includes:
  • Text
  • Sentiment
  • Timestamp
  • Priority: High

How To Customize Nodes

• Webhook Node

  • Change endpoint path as needed

• HTTP Request Node

  • Replace Hugging Face model with another model if required

• Compute Sentiment Node

  • Adjust confidence threshold (currently 0.9)
  • Modify classification logic

• Google Sheets Nodes

  • Change document ID or sheet names
  • Add more columns if needed

• Jira Node

  • Customize issue type, priority or project

Add-ons

You can enhance this workflow with:

• Email or Slack notifications for negative feedback
• Dashboard visualization using BI tools
• Sentiment trend analytics
• Multi-language sentiment analysis
• Integration with CRM systems

Use Case Examples

1. Customer feedback analysis for apps or websites
2. Product review classification from multiple sources
3. Social media sentiment monitoring
4. Support ticket prioritization system
5. Survey response automation

There can be many more use cases depending on how feedback data is collected and used.

Troubleshooting Guide

Need Help?

If you need assistance setting up this workflow, customizing nodes or building advanced automation solutions, feel free to reach out to n8n developers at WeblineIndia.

Our team can help you:

• Deploy n8n workflows on cloud/server
• Customize AI-based automation
• Integrate APIs and enterprise tools
• Build scalable business automation systems

Contact WeblineIndia for expert support and tailored solutions.