Sentiment Analysis for Community Issues
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Analyzes the sentiment of comments on community-contributed issues.
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About This Workflow
Overview
This workflow automatically analyzes the sentiment of comments left on community-contributed issues. It extracts the comments, uses an AI model to determine the sentiment (positive, negative, or neutral), and summarizes the findings. The results are then stored in an Airtable database, providing a historical record of sentiment trends for each issue.
Key Features
- Splits individual issues from a list for processing.
- Extracts and formats comments from issues for sentiment analysis.
- Utilizes an OpenAI model to determine sentiment and provide a summary.
- Stores and updates sentiment analysis results in an Airtable base.
- Tracks previous sentiment for historical comparison.
How To Use
- Import Workflow: Import this JSON into your n8n instance.
- Configure Credentials: Set up your OpenAI API key and Airtable Personal Access Token.
- Airtable Setup: Ensure your Airtable base and table are configured with the expected fields (Issue ID, Current Sentiment, Summary, etc.). The Airtable nodes are pre-configured to connect to a base named "Sentiment Analysis over Issue Comments" and a table named "Table 1". You may need to adjust these to match your setup.
- Trigger Configuration: The Airtable trigger is set to poll your Airtable base every hour. You can adjust this frequency as needed.
- Run Workflow: Once configured, the workflow will automatically process new or updated entries in your Airtable base.
Apps Used
Workflow JSON
{
"id": "117daf8f-8adf-4f15-9611-8324f35cc69b",
"name": "Sentiment Analysis for Community Issues",
"nodes": 0,
"category": "Community Management",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 117daf8f-8adf...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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