Linear Ticket Sentiment Monitoring and Alert Automation Workflow

Workflow Name

Linear Ticket Sentiment Monitoring and Alert Automation Workflow

Key Features and Highlights

This workflow continuously monitors active tickets on the Linear platform by automatically fetching recently updated tickets and their comments. It leverages AI technology to perform sentiment analysis on the comments, determining the overall sentiment status of each ticket (positive, neutral, or negative). The analysis results are stored in an Airtable database for easy aggregation and historical tracking. Crucially, when a ticket’s sentiment shifts from non-negative to negative, the system automatically sends alert notifications via Slack, enabling the team to promptly identify potential customer support risks and prioritize their resolution.

Core Problems Addressed

• Automates the monitoring of sentiment changes across a large volume of tickets, eliminating the need for manual, comment-by-comment review.
• Provides early warnings based on sentiment shifts to quickly identify potentially escalating customer issues, reducing the risk of declining customer satisfaction.
• Centralizes data storage and updates to facilitate data management and multidimensional analysis.

Use Cases

• Customer support teams gain real-time insight into the emotional dynamics of ticket handling.
• Product and project management teams monitor user feedback sentiment to identify potential issues.
• Any business scenario requiring automated responses triggered by sentiment changes in comment content.

Main Workflow Steps

1. Scheduled Trigger: Automatically initiates the workflow every 30 minutes.
2. Data Retrieval: Pulls active tickets updated within the last 30 minutes along with their comments via Linear’s GraphQL API.
3. Splitting: Breaks down the ticket list to process each ticket individually.
4. Sentiment Analysis: Uses OpenAI’s language models to extract sentiment from ticket comments, determining overall sentiment orientation and generating summaries.
5. Data Merging: Combines sentiment analysis results with ticket details.
6. Existing Data Query: Searches Airtable for historical sentiment data corresponding to each ticket.
7. Database Update: Writes the new sentiment status into Airtable, saving the previous status as “prior sentiment” to enable sentiment tracking.
8. Trigger Monitoring: Airtable triggers detect changes in the sentiment field.
9. Sentiment Shift Evaluation: Applies conditional logic to identify tickets whose sentiment changed from non-negative to negative.
10. Deduplication: Prevents duplicate notifications for the same sentiment change on a ticket.
11. Notification Dispatch: Sends detailed Slack messages about sentiment-shifted tickets to designated channels, alerting the team for timely attention.

Involved Systems and Services

• Linear: Source of tickets; uses GraphQL API to fetch tickets and comment data.
• OpenAI: Performs sentiment analysis and summarization of comment content using language models.
• Airtable: Stores and manages ticket sentiment data, enabling historical tracking and trigger-based monitoring.
• Slack: Receives and displays sentiment shift alert notifications to facilitate team collaboration and response.
• n8n: Serves as the automation integration platform coordinating data retrieval, processing, and notification delivery.

Target Users and Value

• Customer Support Teams: Gain real-time awareness of customer sentiment to rapidly address potential negative feedback.
• Product Managers and Project Leads: Obtain insights into user feedback sentiment to optimize product experience.
• Data Analysts: Accumulate sentiment data to support decision-making and trend analysis.
• Technical Operations Personnel: Reduce manual monitoring workload through automation, improving operational efficiency.

This workflow provides teams with an automated and intelligent sentiment monitoring solution, ensuring that problematic tickets are promptly detected and addressed, thereby enhancing customer satisfaction and team collaboration efficiency.