Semantic Feedback Processing with LLMs: Automating Issue Detection and Prioritization in DevOps

This paper introduces an LLM-augmented pipeline for automating the transformation of unstructured Reddit feedback into structured Jira issues. Designed for DevOps environments, the system performs a two-layered sentiment analysis - combining fast, lightweight classification with high-accuracy LLM-based scoring - followed by LLM-driven summarization and component-aware semantic matching. Feedback is either linked to existing issues or triggers new ticket creation based on similarity thresholds and component alignment. A hybrid graph-relational backend supports issue clustering, trend detection, and historical traceability. Based on real-world game development data, the system shows high scalability and significantly improves triage efficiency and developer feedback coverage.