Sentiment Analysis for Linear Support Issues with Slack Alerts

This workflow automates sentiment tracking on active support issues in Linear.app. A scheduled trigger fetches recently updated issues via GraphQL, analyzes comment threads with OpenAI for overall mood, and stores results in Airtable alongside issue details. On subsequent runs, it compares new sentiment against previous states, updating records to track changes over time.

An Airtable trigger monitors updated rows, filtering for issues that shift from non-negative to negative sentiment. Matching cases trigger Slack notifications to the team channel, enabling quick intervention on escalating customer dissatisfaction.

Benefits include early detection of unhappy customers, reducing churn through timely responses, and providing a dashboard in Airtable for sentiment trends across support tickets. Ideal for customer support teams using Linear, it saves hours of manual monitoring and leverages AI for accurate, scalable analysis.

Use cases: Daily operations review, team alerting for priority issues, performance metrics on support quality. Customize GraphQL filters for specific teams or issue types, adjust Slack channels, and adapt for other issue trackers.