Track Support Issue Sentiment in Linear with OpenAI and Slack

## Try It Out! ### This n8n template performs continous monitoring on Linear Issue conversations performing sentiment analysis and alerting when the sentiment becomes negative. This is helpful to quickly identify difficult customer support situations early and prioritising them before they get out of hand. ## 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. ### Need Help? Join the Discord or ask in the Forum! Happy Hacking!