Automate Sentiment Analysis for Project Issues
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This workflow automatically analyzes the sentiment of comments within project issues using AI and stores the results in Airtable. Gain valuable insights into team and customer feedback effortlessly.
About This Workflow
This n8n workflow leverages the power of AI to provide sentiment analysis on project issue comments. It begins by splitting incoming issues into individual items, then processes the comments associated with each issue. Using a sophisticated AI model (OpenAI Chat Model and Langchain's Information Extractor), it identifies the sentiment (positive, negative, or neutral) and generates a summary of the conversation. The workflow then captures key issue details and attempts to retrieve existing sentiment data from an Airtable base. Finally, it updates the Airtable record with the new sentiment analysis, including a comparison to any previously recorded sentiment. This provides a comprehensive historical view of sentiment trends directly within your project management tools.
Key Features
- AI-Powered Sentiment Analysis: Automatically determine the sentiment of issue comments using advanced AI.
- Comment Aggregation: Consolidates all comments for an issue into a single analysis.
- Sentiment Summarization: Generates a concise summary of the overall sentiment of the discussion.
- Airtable Integration: Seamlessly stores and retrieves sentiment data in Airtable for historical tracking.
- Historical Sentiment Tracking: Compares current sentiment with previous sentiment entries to identify trends.
How To Use
- Connect Your AI Provider: Configure the 'OpenAI Chat Model' node with your OpenAI API credentials.
- Define Issue Data Source: Ensure the input data feeding into the 'Issues to List' node contains a
data.issues.nodesarray, where each node has acomments.nodesarray. - Configure Sentiment Extraction: The 'Sentiment over Issue Comments' node is pre-configured to extract sentiment and a summary from the
comments.nodes. - Set Up Airtable Integration: In the 'Get Existing Sentiment' and 'Update Row' nodes, specify your Airtable Base ID (
appViDaeaFw4qv9La) and Table Name (tblhO0sfRhKP6ibS8). Ensure your Airtable table has columns for 'Issue ID', 'Current Sentiment', 'Previous Sentiment', 'Summary', 'Assigned', 'Issue Title', 'Issue Created', and 'Issue Updated'. - Map Issue Data: Adjust the
jsonOutputin the 'Copy of Issue' node if your issue data structure differs from the expectedidentifier,title,createdAt,updatedAt,assignee.name, andsentimentfields. - Map Airtable Fields: In the 'Update Row' node, ensure the
columnsare correctly mapped to your Airtable fields and that theIssue IDis used to match existing records. - Run the Workflow: Trigger the workflow to start analyzing issue sentiment and populating your Airtable base.
Apps Used
Workflow JSON
{
"id": "c7e0a632-b531-44ce-bd95-ddbd3e47656b",
"name": "Automate Sentiment Analysis for Project Issues",
"nodes": 19,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: c7e0a632-b531...
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