Automated Sentiment Monitoring for Customer Support Issues

This n8n workflow automates the process of monitoring customer support issues by integrating Linear, OpenAI, Airtable, and Slack. Its main goal is to continuously track active issues, analyze the sentiment of comments, and alert teams when negativity rises. The workflow begins with a scheduled trigger to fetch recent issues from Linear via GraphQL. Each issue’s comments are then analyzed using OpenAI’s language model to determine overall sentiment. The results, along with issue details, are stored and updated in Airtable, which serves as a sentiment tracking database. An Airtable trigger monitors for sentiment changes, especially transitions from non-negative to negative. When such a transition is detected, a notification is sent via Slack to alert the team of potentially problematic issues, enabling prompt intervention. This automation is particularly useful for support teams aiming to proactively manage customer dissatisfaction and escalate negative feedback early.