This n8n template monitors active support issues in Linear.app to track the mood of their ongoing conversation between reporter and assignee using Sentiment Analysis. When sentiment dips into the negative, a notification is sent via Slack to alert the team.
How it works
- A scheduled trigger is used to fetch recently updated issues in Linear using the GraphQL node.
- Each issue's comments thread is passed into a simple Information Extractor node to identify the overall sentiment.
- The resulting sentiment analysis combined with the some issue details are uploaded to Airtable for review.
- When the template is re-run at a later date, each issue is re-analysed for sentiment
- Each issue's new sentiment state is saved to the airtable whilst its previous state is moved to the "previous sentiment" column.
- An Airtable trigger is used to watch for recently updated rows
- Each matching Airtable row is filtered to check if it has a previous non-negative state but now has a negative state in its current sentiment.
- The results are sent via notification to a team slack channel for priority.
Check out the sample Airtable here: https://airtable.com/appViDaeaFw4qv9La/shrq6HgeYzpW6uwXL
How to use
- Modify the GraphQL filter to fetch issues to a relevant issue type, team or person.
- Update the Slack channel to ensure messages are sent to the correct location or persons.
- The Airtable also serves to give a snapshot of Sentiment across support tickets for a given period. It's possible to use this to assess the daily operations.
Requirements
- Linear for issue tracking (but feel free to use another system if preferred)
- Airtable for Database
- OpenAI for LLM and Sentiment Analysis
Customising the workflow
- Add more granular levels of sentiment to reduce the number of alerts.
- Explore different types of sentiment based on issue types and customer types. This may help prioritise alerts and response.
- Run across teams or categories of issues to get an overview of sentiment across the support organisation.